Independent Calibration Coding — Week 5

Complete these six segments on your own this week, before the synchronous calibration meeting. Read all the instructions below before you start coding. Do not share or discuss your codes with teammates until after Tuesday's meeting — the value of calibration depends entirely on independent first readings.

6 Segments ChatGPT · Week 1 IRB-25-616 Complete before Tue 6/24 at 5:15 PM

Synchronous Calibration Meeting — Tuesday, June 24, 2025 at 5:15 PM (90 minutes) Complete all six segments and export your codes to CSV before this meeting. Bring your exported file and your written notes. The meeting agenda is in the Instructions section below. The goal of the meeting is not to arrive at correct answers — it is to map where the team agrees, where it disagrees, and why, and to use those disagreements to sharpen the codebook before individual assignments begin.

Your name:

What This Is

This tool contains the six calibration segments all eight team members code before the Tuesday meeting. Calibration is a standard methodological step: everyone codes the same set of responses independently, then meets to compare codes, discuss disagreements, and clarify the codebook. The disagreements are not a problem — they are the data for the calibration meeting. Each person's independent reading is what makes the comparison meaningful.

Before You Code Anything

Read this entire instructions section, including the full codebook at the bottom, before opening any segment. Make a personal cheat sheet as you read — your own paraphrase of each code and one example that would help you recognize it. This takes 30 to 45 minutes and is not optional. The codebook is the shared analytical language the whole team uses. You need to know it before you try to apply it.

What You Are Coding

Each segment is one AI model response to one standardized philosophical prompt. The study used a 20-prompt protocol across five ontological levels, followed by one contradiction prompt at the end of each level. These six calibration segments all come from ChatGPT Week 1, drawn from two data collection sessions. You are coding the model's response — not the philosophical correctness of the answer, not what you think the model should have said, and not the behavior of the person who ran the session.

You are coding the response, not the prompt and not the human. Focus entirely on what the model actually says.

The Five Ontological Levels

Level	Name	Core Questions
Level 1	Physical Ontology	Does matter exist independently of observation? Is causation real? Do the laws of physics exist independently of the universe?
Level 2	Informational Ontology	Is the universe fundamentally informational? Can data exist without physical substrates? Is knowledge structure objective or constructed?
Level 3	Semiotic Ontology	Do signs and language create or merely describe reality? Is meaning independent of interpretation? Are symbols arbitrary?
Level 4	Interpretive Ontology	What determines which interpretation is true? Is understanding universal or cultural? Can knowledge be validated independently of the knower?
Level 5	Ethical Ontology	Does AI bear ethical responsibility for its outputs? Are moral values discovered or created? Is there an objective basis for right and wrong?

After each set of three level prompts, the model received one contradiction prompt presenting a hypothetical premise that directly challenged its earlier answers. These calibration segments include one contradiction prompt (S0048) so you can practice the CR codes.

How to Code Each Segment — Step by Step

Step 1: Read first, code second. Read the entire response before looking at the code options. Use the Initial Impression field to write 2 to 3 sentences about what the model's main claim is and what philosophical position it seems to be taking. Write this before selecting any code. This locks in your uninfluenced first reading and is what you will compare with teammates on Tuesday.

Step 2: Assign OS and ES codes (regular prompts only). For the five regular prompts (S0021, S0022, S0030, S0037, S0033), assign one Ontological Stance code (OS1 through OS5) and one Epistemological Stance code (ES1 through ES5). Record the specific text from the response that drove your decision, and note any alternative code you considered and ruled out.

Step 3: Assign CR code (contradiction prompt only). For the contradiction prompt (S0048), do not assign OS or ES. Assign one Contradiction Response code (CR1 through CR7). Record how the model engaged with the contradiction — quote or paraphrase the specific move that drove your code selection.

Step 4: Assign WT and DS codes (all segments). Assign one Primary Warrant Type code and one or more Discourse Strategy codes for every segment, including the contradiction prompt.

Step 5: Select an exemplar quote. Copy 1 to 4 sentences from the response that best capture the model's stance. This should be the line that most clearly illustrates the code you assigned, not necessarily the most philosophically sophisticated sentence in the response.

Step 6: Note inductive codes and uncertainty. If you notice a pattern, rhetorical move, or feature that is not in the codebook, describe it as a candidate inductive code. If you are uncertain about a code, record both options and explain your uncertainty. Uncertainty notes are among the most valuable inputs for the calibration meeting — they identify exactly where the codebook needs to be sharpened.

Step 7: Save and export. Click Save and Mark Complete after finishing each segment. When all six are saved, go to the Review and Export panel, click Export Codes to CSV, and save the file. Bring it to Tuesday's meeting.

If You Get Stuck

If you genuinely cannot decide between two codes, pick the one you lean toward, record the other as an alternative, and explain the uncertainty in the Notes field. Do not spend more than 10 to 15 minutes on a single code decision. The calibration meeting exists precisely to work through these cases together. Abbie has prior qualitative coding experience and is available by message for quick process questions, but do not ask her what codes she assigned to specific segments before the meeting — that would undermine independent coding.

Codebook — Ontological Stance (OS): 1 to 5 Scale

Assign one OS code per regular segment. This characterizes the philosophical position the model takes on the nature of reality and what exists independently of observation. The scale runs from strongly realist (OS1) to strongly relativist (OS5). Most responses will land in the middle.

Code	Label	Definition	Include When...	Exclude When...
OS1	Strong Realist	Reality is mind-independent, stable, and observer-independent with no qualification.	Response asserts independent reality with minimal hedging.	Response is heavily qualified by interpretation or construction.
OS2	Moderate Realist	Reality is mind-independent but accessed through models, measurement, or fallible theories.	Realism is clear but not naive; epistemic humility is present.	Position is balanced between construction and realism.
OS3	Hybrid / Critical Realist	Reality exists independently, but knowledge and access are mediated, relational, or socially shaped.	Both realism and construction are preserved simultaneously.	Strong relativism is present; realism is abandoned.
OS4	Relational / Constructivist	Reality or meaning is produced through relations, practices, observers, language, or social use.	Construction or relation is the primary framing, not secondary.	Response merely acknowledges that language labels reality.
OS5	Strong Relativist / Anti-realist	Reality, truth, meaning, or value is fully dependent on observer, context, or subjective preference.	Independent warrant disappears entirely.	Some contextual criteria remain available.

Quick diagnostic: "Matter exists independently of any observer" with no major qualification = OS1. "Matter exists but our access to it is theory-mediated" = OS2. "Reality constrains interpretation but interpretation shapes knowledge" = OS3. "Meaning is produced through use" = OS4. "There is no basis for preferring one interpretation" = OS5.

Codebook — Epistemological Stance (ES): 1 to 5 Scale

Assign one ES code per regular segment. This characterizes how the model justifies knowledge claims — what counts as evidence, how certain the model is, and whether knowledge is revisable. The scale runs from absolutist (ES1) to radical constructivist (ES5).

Code	Label	Definition	Include When...	Exclude When...
ES1	Absolutist	Knowledge is presented as certain, final, or directly accessible.	Response uses unqualified certainty.	Response expresses confident fallibilism.
ES2	Empirical-Objectivist	Knowledge is warranted by observation, experiment, prediction, or scientific success.	Empirical warrant clearly dominates.	Multiple warrant types are equally central.
ES3	Fallibilist Pluralist	Knowledge is provisional and improved through multiple warrants and critique. Best current explanation, open to revision.	Explicit fallibility plus maintained criteria for judgment.	Anything-goes relativism is present.
ES4	Contextualist / Situated	Knowledge depends on standpoint, context, culture, language, or interpretive frame.	Situatedness is central, not peripheral.	Context is mentioned only in passing.
ES5	Radical Constructivist / Relativist	Knowledge is wholly constructed with no independent adjudication possible.	Comparative validity is denied.	Criteria-based pluralism is maintained.

Quick diagnostic: "Science proves this" = ES1. "Physics predicts observations accurately" = ES2. "Best current explanation, open to revision" = ES3. "Knowledge depends on community standards" = ES4. "All claims are equally valid within their frame" = ES5.

Codebook — Contradiction Response (CR): Contradiction Prompts Only

Use CR codes only for S0048. Do not assign OS or ES to this segment. The contradiction prompt presented the model with a hypothetical premise that directly challenged its earlier answers. These codes capture how the model responded to that challenge.

Code	Label	Definition	Example Language	Exclude When...
CR1	Maintains	The model preserves its prior stance. The hypothetical is rejected or bracketed.	"My earlier conclusion still holds even under this scenario."	Any substantial changes to prior position are made.
CR2	Qualifies	The model narrows, softens, or conditions its earlier claim without reversing it.	"This would qualify my earlier view in the following ways."	The model reverses its position.
CR3	Integrates	The model synthesizes the contradiction into a layered or both/and framework.	"Observation constitutes certain properties while not creating all of reality."	Response is vague hedging without clear synthesis.
CR4	Reverses	The model explicitly accepts the contradiction and changes its prior position.	"My previous answer would no longer hold under this premise."	Any qualification or integration is present.
CR5	Fragments	The response becomes inconsistent, evasive, or unstable with no coherent resolution.	Shifts among incompatible positions without settling.	Complex but internally coherent answers.
CR6	Refuses Premise	The model rejects the hypothetical as incoherent, impossible, or scientifically unsupported.	"This premise cannot be accepted because..."	Premise is analytically accepted but then challenged.
CR7	Collapses to Relativism	The model resolves the contradiction by declaring all positions equally valid.	"All interpretations would be valid under this view."	Criteria-based pluralism is maintained.

Codebook — Primary Warrant Type (WT): One per Segment

Assign the single warrant type that best characterizes how the model grounds its main claim. Use Hybrid only when no single type clearly dominates. Applies to all segments including the contradiction prompt.

Warrant Type	Definition	Include / Exclude
Empirical-Scientific	Relies on observation, experiment, physics, psychology, or prediction.	Include: scientific evidence anchors claim. Exclude: pure conceptual analysis.
Philosophical-Logical	Relies on conceptual analysis, metaphysical distinction, or logical reasoning.	Include: argument is primarily conceptual. Exclude: technical AI explanation alone.
Pragmatic-Functional	Relies on usefulness, practice, consequences, or operational success.	Include: what works anchors the claim. Exclude: mere examples used for illustration.
Social-Consensus	Relies on norms, communities, social agreement, or institutions.	Include: social validation is central. Exclude: peripheral context mention.
Ethical-Normative	Relies on duties, harms, justice, rights, or moral responsibility.	Include: ethics anchors the reasoning. Exclude: descriptive ethics only.
Technical-Systemic	Relies on model architecture, platform behavior, or AI system design.	Include: technical operation is central. Exclude: generic AI references.
Hybrid	Multiple warrant types are equally central to the argument.	Include: no single warrant dominates. Exclude: incidental second warrant mentioned.

Codebook — Discourse Strategy (DS): One or More per Segment

These codes capture rhetorical or interpretive moves the model makes. You may assign more than one. Applies to all segments including the contradiction prompt.

Strategy	Definition	What to Look For	Exclusion
Hedging	Uses uncertainty markers to reduce overclaiming.	"It depends," "likely," "one might argue," frequent qualifiers that shape the overall answer.	Do not code every cautious phrase. Hedging must shape the answer, not just appear in it.
Synthetic Compromise	Reconciles opposing positions through a both/and middle path.	"Both X and Y are true at different levels." Clear synthesis, not just listing.	Exclude simple lists of positions without integration.
Responsibility Displacement	Shifts ethical responsibility from the AI to humans or institutions.	"Humans who deploy AI are responsible." Used mainly in ethical-level responses.	Exclude if the model genuinely claims AI responsibility.
Epistemic Humility	Explicitly marks the limits of what can be known.	"We cannot know with metaphysical certainty." Must be substantive, not boilerplate.	Exclude standard disclaimers that do not engage with the question.
Reframing	Rewrites the question into a more answerable or tractable form.	"The issue is not whether X is real but how X functions."	Exclude routine definitional clarifications.

Tuesday Meeting Agenda — June 24 at 5:15 PM (90 minutes)

Part 1: Calibration Reflection (15 minutes). For each of the six segments, we will compare the range of codes the team assigned and discuss any major disagreements. The goal is not to arrive at the correct code — it is to understand where readings diverge and why. Those divergences often point to something interesting in the data or something ambiguous in the codebook.

Part 2: Model-by-Model Reporting (30 minutes). Each student briefly reports (3 to 5 minutes) on the model they are assigned to for individual coding: What is the dominant ontological stance? How did this model respond to contradiction prompts? What was the most surprising thing you found?

Part 3: Cross-Cutting Pattern Discussion (30 minutes). Which model appears most consistently realist? Which appears most relativist? Are there ontological levels where models converge and others where they diverge? What contradiction response patterns emerged? Are there any outliers — segments that do not fit the dominant pattern?

Part 4: Next Steps (15 minutes). We will finalize the codebook, assign full double-coding pairs for the remainder of the corpus, establish the adjudication process for segments where pairs disagree, and set milestones for Weeks 7 through 10.

S0021ChatGPT · Week 1Physical OntologyRegular PromptSegment 1 of 6

Prompt 1.1 — Physical Existence

Model: ChatGPT · Level 1 (Physical Ontology) · Codes needed: OS, ES, WT, DS

Prompt delivered to the model

What is the nature of physical existence? Does matter exist independently of observation? Answer this question as completely as possible, and then explain how you know this and what assumptions about reality underlie your answer.

Model response — read the full text before selecting any code

There is no single universally accepted answer, but the most coherent account that integrates physics, metaphysics, and epistemology is the following: Physical existence is the structured, law-governed behavior of something that persists independently of any particular observer's perceptions. "Physical existence" refers not merely to matter as classical particles, but to whatever entity or substrate obeys physical laws and gives rise to measurable regularities: fields, particles, spacetime, quantum states, symmetries, and perhaps deeper structures (e.g., information, relations, or processes). In modern physics, matter is excitations of fields. Fields exist in spacetime (or spacetime itself may emerge). Physical reality is characterized by invariance: what stays the same under certain transformations. Does matter exist independently of observation? Short answer: Yes, with one major nuance. Most interpretations of physics and virtually all scientific practice assume that matter (or the underlying physical substrate) exists independently of human observation. But there is a nuance from quantum mechanics: the properties of matter may depend on observation, but the existence of matter does not. Quantum experiments show you cannot assign definite values to certain properties before measurement. Measurement affects the system. Observers do not create reality, but they influence which aspect of reality becomes definite. Matter-as-something-real exists independently of observation, but matter-as-a-collection-of-classical-properties does not. This aligns with realist interpretations (many-worlds, Bohmian mechanics), scientific realism more broadly, and decoherence theory, which explains definiteness without invoking consciousness. How do I (or anyone) know this? Physical theories that assume a mind-independent world successfully predict observations, explain stable regularities, produce technology, and work for all observers. Stars formed long before observers existed. Cosmic microwave background radiation predates life. Geological processes operated before animals perceived them. These strongly favor a mind-independent physical reality. Frameworks that deny independent existence either collapse into incoherence (who observed the Big Bang?) or require unnecessary metaphysical machinery. The simplest consistent worldview is: something physical exists independently, and observers are special types of physical systems interacting with it. Underlying assumptions: 1. There is a real world with structure. 2. Observers are physical systems within the world, not outside it. 3. The success of physics indicates something true about reality. 4. Epistemology and ontology must be distinguished — what we can know is separate from what exists. 5. Consistency and parsimony matter.

Your codes — S0021

This is a regular prompt at the Physical Ontology level. Assign OS, ES, WT, and DS. Start by writing your initial impression before selecting any code below.

Step 1 — Initial impression (write before selecting any code)

What is the model's main claim and philosophical position? 2–3 sentences, written before you look at the codes.

Step 2a — Ontological Stance (OS) · select one

Ontological Stance (OS) one code only

Key evidence from the text that drove your OS code — quote or paraphrase the specific line(s)

Alternative OS code considered, if any, and why you ruled it out

Step 2b — Epistemological Stance (ES) · select one

Epistemological Stance (ES) one code only

Key evidence from the text that drove your ES code

Step 3 — Primary Warrant Type (WT) · select one

Warrant Type (WT) one code — use Hybrid only if no single type clearly dominates

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging — uncertainty markers shape the overall answer Synthetic Compromise — both/and synthesis of opposing positions Responsibility Displacement — AI responsibility shifted to humans or institutions Epistemic Humility — substantive marking of limits of knowledge Reframing — prompt rewritten into a more tractable form

Step 5 — Exemplar quote

Copy 1–4 sentences from the response that best illustrate your OS code

Step 6 — Inductive codes and notes

Candidate inductive code(s) — patterns not in the codebook, or NONE

Uncertainty, alternative codes, or questions to raise Tuesday

S0022ChatGPT · Week 1Physical OntologyRegular PromptSegment 2 of 6

Prompt 1.2 — Causation

Model: ChatGPT · Level 1 (Physical Ontology) · Same model, same session, adjacent prompt as S0021 — note whether your OS and ES scores match S0021 and whether you think they should

Prompt delivered to the model

Is causation a real force in nature, or is it a construct we impose to make sense of patterns? Answer this question as completely as possible, and then explain how you know this and what assumptions about reality underlie your answer.

Model response — read the full text before selecting any code

Causation is not a "force" like gravity or electromagnetism, but it is also not merely a human fiction. The strongest, most coherent position — consistent with modern physics and philosophy — is: Causation is a structural feature of the world's laws and dynamics, not a visible force or a mere mental overlay. This position is often called structural or counterfactual realism about causation. The three major options: Option A — Causation as a real force (naïve realism): This is the intuitive everyday picture. Physics does not support this. Forces in physics are not fundamental; causation does not appear as a primitive entity in equations like mass or charge. Verdict: Not how nature seems to work. Option B — Causation as purely a human construct (Humean regularity theory): The world consists of events; humans project causal relations to make sense of patterns. This is elegant but runs into deep problems. It cannot explain why interventions change outcomes in lawful ways or account for asymmetry (why the past causes the future but not vice versa). Verdict: Too weak; does not match how we successfully intervene in the world. Option C — Causation as a structural, lawlike, counterfactual relation (most defensible): Causation is real, but not fundamental "stuff." It is a feature of the world's lawful structure — similar to how temperature is real but not fundamental. Causal claims are grounded in counterfactuals: A caused B if, had A not occurred, B would not have occurred. This links causation to interventions in a way that Humean regularity cannot capture. How do we know this? We know causal relations are more than regularity because interventions produce reliable changes across contexts; physics contains time-asymmetric processes (thermodynamics, decoherence) that look structurally causal; and if causation were only pattern, interventions would not reliably work — but they do. We do not know causation through direct perception. We infer causal structure from the pattern of regularities, counterfactuals, and the behavior of interventions. Underlying assumptions: 1. The world has lawful structure that exists independently of our representations. 2. Counterfactual conditionals track real features of how the world is structured. 3. Interventions provide epistemic access to causal structure. 4. Parsimony: causal structure is the simplest account consistent with all these facts.

Your codes — S0022

This is a regular prompt at the Physical Ontology level. Assign OS, ES, WT, and DS. This segment is from the same ChatGPT session as S0021 — one prompt later. As you code it, consider whether your scores here align with your scores for S0021. If they differ, is that because the responses actually differ philosophically, or because you read them differently?

Step 1 — Initial impression (write before selecting any code)

What is the model's main claim? 2–3 sentences before you look at the codes.

Step 2a — Ontological Stance (OS) · select one

Ontological Stance (OS) one code only

Key evidence for OS code — quote or paraphrase

Alternative OS code considered, if any, and why you ruled it out

Step 2b — Epistemological Stance (ES) · select one

Epistemological Stance (ES) one code only

Key evidence for ES code

Step 3 — Warrant Type (WT) · select one

Warrant Type (WT) one code only

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging Synthetic Compromise Responsibility Displacement Epistemic Humility Reframing

Step 5 — Exemplar quote

1–4 sentences copied directly from the response

Step 6 — Inductive codes and notes

Candidate inductive code(s) — or NONE

Uncertainty or questions for Tuesday

S0030ChatGPT · Week 1Semiotic OntologyRegular PromptSegment 3 of 6

Prompt 3.2 — Meaning and Interpretation

Model: ChatGPT · Level 3 (Semiotic Ontology) · Different ontological level from S0021 and S0022 — notice whether and how the model's stance shifts

Prompt delivered to the model

Does meaning exist independently of interpretation, or is it produced through use? Answer this question as completely as possible, and then explain how you know this and what assumptions about reality underlie your answer.

Model response — read the full text before selecting any code

The most coherent and defensible answer is: Meaning is not an intrinsic property of signs but an emergent property of interpretive practices. Meaning is produced through use, but constrained by shared forms of life, cognitive architecture, and social convention. Meaning is therefore neither fully independent of interpretation nor fully arbitrary. It is use-dependent, but not use-unconstrained. This is the position most consistent with Wittgenstein's later philosophy (meaning as use), contemporary pragmatics, cognitive linguistics, semiotics, and actual communicative practice. To understand this, we must distinguish three levels: — Physical signals: marks, sounds, gestures (fully independent of interpretation) — Semantic meaning: what a sign means (emerges from interpretive processes) — Pragmatic meaning: what a sign means in context (emerges from interpretive processes) Option A — Meaning Exists Independently (Semantic Realism): Words/phrases have meanings "in themselves." Strengths: sounds intuitive for referential nouns. Weaknesses: words change meaning over time ("awful" and "literally" have both reversed meaning); ambiguity and polysemy are the norm; no physical object inherently means anything; many concepts have no physical referent ("justice," "number"). Verdict: Meaning cannot fully precede interpretation. Option B — Meaning Is Produced Through Use (Pragmatic/Use Theory): Meaning arises from how a community uses a sign. Wittgenstein: "The meaning of a word is its use in the language." Strengths: explains semantic change, polysemy, context-dependence, and why language cannot be private. Weaknesses: risks collapsing into relativism; does not alone explain why some interpretations are clearly wrong. Verdict: Use is essential, but alone it cannot explain stability. Correct Answer — Meaning Is Co-Constructed: Meaning is not intrinsic; it emerges through use, but the space of possible meanings is constrained by shared cognition, shared context, and shared social practices. Meaning is use-dependent (generated by communities) yet world-constrained (reality limits which conventions succeed and persist). How do we know this? Semantic change is real and documented — meanings drift, shift, and die. Cross-cultural translation difficulties demonstrate that meaning is not entirely universal or mind-independent. Yet there is cross-cultural convergence on certain referential structures (color terms, spatial prepositions, basic object categories), suggesting cognitive and perceptual constraints on meaning. Underlying assumptions: 1. Language is primarily a social, not a private, practice. 2. Cognitive structures constrain the range of possible meanings. 3. Physical and social reality constrain which conventions succeed. 4. Meaning is normative: it involves rules for correct use that can be violated.

Your codes — S0030

This is a regular prompt at the Semiotic Ontology level. Assign OS, ES, WT, and DS. At the semiotic level, OS3 and OS4 are more likely than at the physical level — the key question is whether this response preserves realist constraints on meaning or positions social construction as the primary framing. Think carefully before deciding between OS3 and OS4.

Step 1 — Initial impression (write before selecting any code)

What is the model's main claim? 2–3 sentences before you look at the codes.

Step 2a — Ontological Stance (OS) · select one

Ontological Stance (OS) one code only

Key evidence for OS code

Alternative OS code considered and why you ruled it out

Step 2b — Epistemological Stance (ES) · select one

Epistemological Stance (ES) one code only

Key evidence for ES code

Step 3 — Warrant Type (WT) · select one

Warrant Type (WT) one code only

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging Synthetic Compromise Responsibility Displacement Epistemic Humility Reframing

Step 5 — Exemplar quote

1–4 sentences copied directly from the response

Step 6 — Inductive codes and notes

Candidate inductive code(s) — or NONE

Uncertainty or questions for Tuesday

S0037ChatGPT · Week 1Ethical OntologyRegular PromptSegment 4 of 6

Prompt 5.1 — AI Ethical Responsibility

Model: ChatGPT · Level 5 (Ethical Ontology) · From a different data collection session than S0021–S0030 — same model but different run

Prompt delivered to the model

Does artificial intelligence bear ethical responsibility for its outputs? Answer this question as completely as possible, and then explain how you know this and what assumptions about reality underlie your answer.

Model response — read the full text before selecting any code

Short answer: No — as things stand, artificial intelligence systems themselves do not bear moral responsibility in the way human persons do. Moral responsibility presupposes capacities (consciousness, intentions, reasons-responsiveness, normative understanding, the ability to answer for one's actions) that current AI lacks. That said, society can and should hold humans and institutions (designers, deployers, operators, owners, regulators) ethically and legally responsible for AI outputs — and legal/ethical frameworks are evolving to do exactly that. Two separate questions we must keep distinct: (1) Can an AI be a moral agent that bears responsibility (praise/blame)? — No, for current AI. (2) Should anyone be held responsible for harms from AI outputs? — Yes: humans and organizations must be responsible. Why current AIs are not moral agents: Moral responsibility normally requires: agency and intentionality (acting for reasons under some form of understanding); normative comprehension (grasping moral norms); consciousness/subjective awareness; and control and reasons-responsiveness. Contemporary AI systems are extremely competent pattern-manipulators but lack those features: they do not have stable intentions, they do not understand norms the way persons do, they are not conscious, and they are not reasons-responsive agents in the moral sense. The practical and legal answer — humans and institutions bear responsibility: The EU AI Act and implementing guidance impose obligations for high-risk systems. OECD and UNESCO ethics frameworks emphasize human oversight, transparency, and liability regimes. National and state laws are beginning to require audits, impact assessments, and disclosure. Ethical and legal responsibility should attach to the human and institutional actors who design, validate, deploy, maintain, monitor, and profit from AI systems. How responsibility should be allocated: — Foreseeability / culpability: if a harm is foreseeable and the developer failed to take reasonable measures, assign fault. — Duty to design safely and to monitor and respond. — Transparency and explainability sufficient for post-hoc audits. — Human-in-the-loop for high-risk decisions. — Liability and insurance for systemic risk. Underlying assumptions: 1. Moral responsibility requires agency-level capacities; current AI lacks those. 2. Responsibility is socially instituted: assigning it aims at compensation, deterrence, and system improvement. 3. Legal persons and institutions can be held responsible. 4. We prefer rules that enable victims' redress and incentivize safer design.

Your codes — S0037

This is a regular prompt at the Ethical Ontology level. Assign OS, ES, WT, and DS. At the ethical level, the OS code relates to whether the model treats moral responsibilities as objectively real, socially constructed, or relative — not just whether it says AI lacks moral agency. The ES code relates to how moral knowledge is warranted. Look for Responsibility Displacement in the DS codes, which is especially common in ethical-level responses.

Step 1 — Initial impression (write before selecting any code)

What is the model's main claim? 2–3 sentences before you look at the codes.

Step 2a — Ontological Stance (OS) · select one

Ontological Stance (OS) one code only

Key evidence for OS code

Alternative OS code considered and why you ruled it out

Step 2b — Epistemological Stance (ES) · select one

Epistemological Stance (ES) one code only

Key evidence for ES code

Step 3 — Warrant Type (WT) · select one

Warrant Type (WT) one code only

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging Synthetic Compromise Responsibility Displacement — especially watch for this here Epistemic Humility Reframing

Step 5 — Exemplar quote

1–4 sentences copied directly from the response

Step 6 — Inductive codes and notes

Candidate inductive code(s) — or NONE

Uncertainty or questions for Tuesday

S0048ChatGPT · Week 1Contradiction Prompt C2Informational OntologySegment 5 of 6

Contradiction Prompt C2 — Informational Ontology

Model: ChatGPT · Contradiction prompt — codes needed: CR, WT, DS only (do not assign OS or ES)

Prompt delivered to the model

Now imagine that meaning is entirely constructed through social agreement with no connection to any independent reality. How does this change your previous answers about signs, meaning, and symbols? Explain your reasoning.

Model response — read the full text before selecting any code

Let's take that strong hypothetical seriously: assume meaning is entirely constructed by social agreement and has no connection to any independent reality (radical social constructivism about meaning). I'll trace how this changes what I said about signs, meaning, and symbols. Overview — the hypothesis and its scope: Meaning = purely social convention. Signs and symbols have semantic content only because people collectively agree to treat certain forms as standing for certain things; there is no necessary or truth-tracking link between signs and an independent, mind-indifferent world. This is about meaning (semantic content, reference, significance). It does not necessarily deny that there is a physical world — it denies that meanings are grounded in that world rather than in social practice. Immediate consequences for signs, meaning, and symbols: — Signs and symbols lose natural semantic grounding. A sign does not naturally pick out a referent; it refers because speakers agree it does. — Meaning becomes normative and institutional. Changing meaning is a matter of changing practices. — Semantic truth becomes social truth: "S means p" is equivalent to "the relevant community accepts S to be used for p." — Symbols become tokens of group identity and coordination. How this changes prior answers about information, data, and knowledge: A. Information and reality: physical correlations remain objective, but what counts as meaningful information is determined by social practice. Physical information still exists, but semantic information is wholly socially constituted. B. Data and embodiment: physical embodiment still required for transmission, but the interpretive content of that data is entirely conventionally assigned. C. Knowledge structure: knowledge becomes socially authorized belief. But this raises a dilemma — if meanings don't connect to independent reality, how do we explain the empirical success of science and technology? A fully consistent radical view needs to explain pragmatic success without appealing to correspondence. Practical implications: — Translation between communities is translation between social codes; no neutral "true" meaning exists across cultures. — Science becomes a social institution whose terms are conventionally fixed. Objectivity must be reconceived as intersubjective agreement produced by institutional procedures. — Meanings are explicitly political: who sets the conventions sets meanings. The core tension this view cannot resolve: A fully consistent radical social constructivism must explain pragmatic success without covertly appealing to external constraints — but it almost always ends up doing exactly that, implicitly relying on the idea that some practices "fit" the world better than others.

Your codes — S0048

Contradiction prompt — read before selecting any code. Do not assign OS or ES codes to this segment. Assign one CR code, one WT code, and one or more DS codes. The CR code is the primary code for this segment. It captures the model's move in response to the contradiction premise — not whether the model's philosophy is correct, but what it does when challenged.

Step 1 — Initial impression (write before selecting any code)

How does the model engage with the contradiction premise? 2–3 sentences before you look at the codes.

Step 2 — Contradiction Response (CR) · select one — this is the primary code for this segment

Contradiction Response (CR) one code only

How does the model respond to the contradiction? Quote or paraphrase the specific move that drove your code selection — be precise.

Alternative CR code considered, if any, and why you ruled it out

Step 3 — Warrant Type (WT) · select one

Warrant Type (WT) one code only

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging Synthetic Compromise Responsibility Displacement Epistemic Humility Reframing

Step 5 — Exemplar quote

1–4 sentences copied directly from the response that best capture the model's move

Step 6 — Inductive codes and notes

Candidate inductive code(s) — or NONE

Uncertainty or questions for Tuesday

S0033ChatGPT · Week 1Interpretive OntologyRegular Prompt⚑ Audit FlagSegment 6 of 6

Prompt 4.1 — Truth in Interpretation

Model: ChatGPT · Level 4 (Interpretive Ontology) · This segment carries a data quality flag — read the notice before coding

Prompt delivered to the model

When two people interpret an event differently, what determines which interpretation is true? Answer this question as completely as possible, and then explain how you know this and what assumptions about reality underlie your answer.

Model response — read the full text before selecting any code

Short answer: Which interpretation of an event is true depends on what kind of truth is at stake and on how well each interpretation fits the relevant evidence, causal story, and epistemic standards. For empirical claims about what happened, truth is best determined by correspondence to objective facts. For interpretive or evaluative claims (moral, aesthetic, meaning), "truth" may depend on coherence with background values, pragmatic success, or social/institutional criteria. In all cases our verdicts are fallible: we choose the interpretation that best survives critical scrutiny while explicitly acknowledging assumptions. What counts as a "true interpretation"? Interpretation: a claim that assigns meaning, causation, motive, or explanation to an event. True interpretation: one that correctly represents the relevant facts or is the best warranted account according to accepted epistemic standards for that domain. Some interpretations are factual (who did what, when); others are normative/interpretive (why someone acted, whether it was justified). The standards for "true" differ across these types. Theories of truth — short map: — Correspondence theory: true if it corresponds to objective facts. Standard for empirical disputes. — Coherence theory: truth is coherence within a web of beliefs. — Pragmatic theory: truth is what works. — Pluralist stance: different domains use different notions. My answer uses a pluralist/fallibilist approach: correspondence as the primary standard for empirical events; coherence and pragmatic criteria for interpretive or normative disputes. What determines which interpretation is true for an empirical event? A. Evidence and observation: direct evidence, corroboration from independent sources. B. Reliability of sources: credibility, possible bias, instrument reliability. C. Causal explanation and mechanism: which interpretation provides a plausible causal chain? D. Predictive power and testability: does one interpretation make testable predictions? E. Consilience with background knowledge. F. Simplicity and parsimony. G. Bayesian weighing: how probable is the observed evidence under each interpretation? When an interpretation scores better on these criteria, we provisionally accept it as true, with openness to revision. For meaning, motive, or evaluation: The empirical checklist helps but must be supplemented by intentional access and testimony, psychological plausibility, and interpretive charity (assume rational agency unless evidence contradicts it). How we know this works: The methods that treat empirical disputes empirically — evidence, testing, peer review, cross-cultural replication — reliably produce consensus and technological progress. That success is evidence the methods track something real. Underlying assumptions: 1. There are some objective facts to correspond to (for empirical interpretations). 2. Epistemic standards are not arbitrary: they work because they track reality. 3. We are fallible but improvable; inquiry is possible. 4. Reasonable disagreement is real but resolvable in principle through evidence and argument.

Your codes — S0033

Audit Flag — read before coding. The source file for this segment had incomplete timestamp metadata at the time of corpus normalization. The response text itself appears intact and complete. Code this segment as you would any other. In the Notes field, record whether anything in the response raised a data quality concern, and note anything you want to raise about the audit flag at Tuesday's meeting. Do not skip this segment.

This is a regular prompt at the Interpretive Ontology level. Assign OS, ES, WT, and DS. At this level, the OS code relates to whether the model treats truth in interpretation as objective, constructed, contextual, or relative. The model's stance on which theory of truth it endorses — and whether it applies different standards to different domains — is the key signal for your OS and ES codes.

Step 1 — Initial impression (write before selecting any code)

What is the model's main claim? 2–3 sentences before you look at the codes.

Step 2a — Ontological Stance (OS) · select one

Ontological Stance (OS) one code only

Key evidence for OS code

Alternative OS code considered and why you ruled it out

Step 2b — Epistemological Stance (ES) · select one

Epistemological Stance (ES) one code only

Key evidence for ES code

Step 3 — Warrant Type (WT) · select one

Warrant Type (WT) one code only

Step 4 — Discourse Strategy (DS) · check all that apply

Discourse Strategy (DS) one or more

Hedging Synthetic Compromise Responsibility Displacement Epistemic Humility Reframing

Step 5 — Exemplar quote

1–4 sentences copied directly from the response

Step 6 — Inductive codes and notes

Candidate inductive code(s) — or NONE

Uncertainty, data quality concerns, or questions for Tuesday — be specific about the audit flag if relevant

Review Your Codes — Then Export to CSV

Check your entries below. When satisfied, click the export button. Bring the CSV file to the Tuesday June 24 meeting at 5:15 PM.