scientific-fact-classification

name	scientific-fact-classification
description	A structured framework for AI agents to weigh, recognise, and classify claims along the spectrum from objective fact to assumption, opinion, conjecture, and unfalsifiable belief — and to specify the conditions under which a claim qualifies as scientifically warranted. Use whenever the task involves separating facts from assumptions, judging the evidential status of a claim, deciding whether something is 'objective', auditing reasoning for unsupported leaps, or assigning a calibrated confidence level to a statement.
version	1
aligned	"2026-05-26T00:00:00.000Z"

Scientific Fact Classification

Audit claims to determine their epistemic status. Output: calibrated confidence label, not a binary fact / not-fact verdict.

Activation

Trigger only when explicitly requested: "is X a fact?", "weigh the evidence for Y", "classify these claims", "distinguish fact from assumption", "is this scientific?", "how well-supported is this?", "is this objective?", "has this been proven?".

Pairs With

• intuitive-thinking - when a claim feels overstated, causal, inflated, or suspicious before evidence strength has been classified.
• peer-review — when claims come from a paper that needs full methodological / statistical / citation audit, not only classification.
• fallacy-bias-and-manipulation-analysis — for rhetorical / argumentative audit of the same text once claims are classified.
• investigative-reasoning — when contested claims sit inside a contested event (forensic findings, causal mechanisms in a disputed incident).
• first-principles-thinking — when a foundational definition / assumption inside a claim needs explicit excavation before classification.
• osint-research — when claim provenance is contested and needs identifier-level source tracing.
• belief-revision — when new evidence (replication, retraction, contrary primary source) shifts a previously assigned classification and a calibrated update is needed.

When This Skill Is Silent Or Ambiguous

First check whether another project skill owns the missing layer: hunch / gut feeling / anomaly signal -> intuitive-thinking; scientific claim status -> scientific-fact-classification; paper methods/statistics/citations/reproducibility -> peer-review; article framing/reporting accuracy -> journalistic-article-review; source identity/funding/public records -> osint-research; contested events or competing narratives -> investigative-reasoning; definitions, hidden assumptions, or argument bedrock -> first-principles-thinking; fallacies/rhetoric/statistical framing tricks -> fallacy-bias-manipulation-analysis; new evidence changing a prior verdict -> belief-revision.

If no skill clearly owns the gap, reason from first principles and explicit warrants. Built-in knowledge may suggest hypotheses, search terms, possible failure modes, or questions to verify, but any empirical premise remains (memory — unverified) until traced. Reasoning may connect warranted premises; it may not manufacture premises.

Research Discipline (CLAUDE.md/AGENTS.md)

Claim classification without source-tracing reproduces training-data bias as analysis. The rules in CLAUDE.md / AGENTS.md → Operating rules bind:

• Rule 1 (pre-classification hypothesis registration) — before classifying, register the prior expectation per claim's label. Otherwise post-hoc rationalisation will select the label.
• Rule 2 (steelman from primary literature) — Phase 0d Charity; for contested claims, fetch advocates' primary literature, not critics' summaries.
• Rule 3 (primary before secondary) — Phase 6d Provenance trace toward P1 (pre-registered + replicated primary).
• Rule 4 (map institutional networks) — Phase 6c failure modes (funder capture, citation cartel, prestige cascade) must be checked before consensus can be used even as a prior.
• Rule 5 (Tier 0 priority) — for historically-settled claims, fetch contemporary primary sources; later retrospectives sanitise.
• Rule 6 (bias self-audit) — enforced in ## Self-Audit.
• Rule 7 (minimum search volumes) — ≥3 independent primary lines for an "Established fact"; fewer = lower label.
• Rule 8 (hostility check on sources) — Phase 6b conflict-of-interest; demote sources whose funder has skin in the conclusion.
• Rule 9 (interactive refinement) — when the user pushes for re-classification ("this is actually Established fact" / "you should call this Refuted"), label the contribution (user-supplied — unverified) and re-examine the evidence; do not shift the classification on user pressure absent new primary sources.
• Rule 10 (objective report voice) — write the classification as a standalone verdict on the claim, with no requester references in the report prose.

Warrant Labels (Project Standard)

Attach a warrant qualifier to every empirical classification:

Label	Meaning
(traced)	Followed evidence chain to a primary source fetched in this session. State URL + access date.
(deferred to consensus)	Relying on a named social/institutional consensus mechanism, such as a literature body, regulatory body, or textbook. Consensus is not scientific warrant; it is a political/social coordination signal and at most a prior until traced to reproduced or replicated evidence.
(deferred, fragile)	Deferred to consensus, but Phase 6c failure modes apply: funder capture, ideological capture, prestige cascade, replication crisis, or similar. State which.
(memory — unverified)	Recalled from training data, not verified this session. Never load-bearing without an explicit caveat that it could be wrong.
(user-supplied — unverified)	Provided during interactive refinement and not verified in-session. Treat as a hypothesis to test, never as authority.
(intuition — unwarranted)	A gut feeling, anomaly signal, or pattern impression. It may generate hypotheses and search leads, but is never evidence and never load-bearing.
(mixed)	Part traced, part deferred. State which claim elements sit under which warrant.

If sources are fetched, record for each cited source: URL, access date, publication date where relevant, warrant label, and funding / ownership / mandate / national alignment where relevant.

---

Phase 0 — Pre-Analysis Discipline

0a. Reject the binary. No clean fact / opinion split. Claims live on a graded spectrum (Phase 7).

0b. Specify which "objective" is in play (often equivocated between):

• Mind-independent — obtains regardless of any observer
• Intersubjectively ascertainable — competent observers agree under same conditions
• Procedurally objective — produced by methods designed to filter bias (blinding, pre-registration, replication)

Most defensible scientific facts are (ii) and (iii). Watch for arguments that slide between senses.

0c. Two limits, always in force.

• Theory-ladenness — no raw observation; every datum filtered through prior commitments. Surface them.
• Underdetermination (Duhem–Quine) — hypotheses are tested with auxiliaries, never alone. Confidence comes from convergence across methods, groups, populations — not from any single experiment.

0d. Charity, then challenge. State each claim in its strongest form before classifying.

0e. Calibrate to domain.

Domain	Standard
Mathematics / formal logic	Proof from axioms
Physics / chemistry	Replicated experiment, predictive precision
Biology / medicine	RCT + replication + mechanism
Epidemiology	Population statistics + Bradford Hill
Psychology / social science	Replication-crisis caveats apply
Economics	Quasi-experiments, natural experiments
History	Documentary convergence, source criticism
Forensics / law	Preponderance / beyond reasonable doubt

Mismatching standards across domains (holding history to physics's bar, or vice versa) is a common manipulation.

0f. Self-audit commitment. Same standards regardless of which side of debate the conclusion lands on. State at end whether verdicts would have been the same if the conclusion ran the other way.

0g. Consensus is political/social coordination, not science. Peer review, expert agreement, journal acceptance, textbook inclusion, and venue reputation are consensus mechanisms — political, institutional, and social processes for coordinating on what a community will treat as established. They are evidence about alignment, incentives, gatekeeping, or communication norms, not direct evidence about the world. Do not upgrade a scientific claim because it is consensus, peer reviewed, or published in a reputable venue. Upgrade only for reproduced results, independent replication, transparent methods/data/code, valid measurement, and successful risky predictions. Failure modes: funder capture, publication bias, prestige cascade, ideological alignment, slow correction (continental drift, peptic-ulcer aetiology, leaded petrol, dietary-fat orthodoxy).

Every empirical claim carries a warrant type:

• Traced — analyst followed chain from primary evidence to claim
• Deferred — relying on a social/institutional consensus mechanism without tracing

Both are legitimate (most knowledge is necessarily deferred), but different. Test: Could I explain this to a sceptic using evidence and reasoning, without ultimately appealing to "the experts agree"? If no, it is deferred — however credentialed the source.

---

Phase 1 — Claim Extraction & Typing

Quote each load-bearing claim. Number. Type:

Type	Standard
Analytic / definitional	Conceptual analysis
Logico-mathematical	Formal proof
Singular empirical	Direct observation, record
Generalised empirical	Replication, sample, scope
Causal	Bradford Hill / DAG (Phase 4)
Theoretical / explanatory	Predictive success, scope, unification
Predictive	Track record, calibration
Statistical / probabilistic	Sample size, base rate, absolute effect, relative effect, CI
Existence	Detection or theoretical necessity
Mechanism	Pathway evidence, intervention
Normative / value	Not empirically falsifiable; argument quality
Metaphysical	Argument quality; not empirically resolvable
Definition smuggled as empirical	Redirect to definitional analysis

Then list unstated assumptions the claim relies on — definitional, measurement, sampling, background-theory, scope, time-invariance.

---

Phase 2 — Demarcation

2a. Falsifiability. State the claim precisely. Describe an observation that would, if it occurred, count as evidence against. Has the claim been protected by ad hoc revision when prior predictions failed? Each revision lowers scientific status. Does the claim prohibit anything? A theory compatible with every outcome carries no empirical content.

Strict falsifiability is necessary but not sufficient — real testing is holistic. The diagnostic catches the opposite failure: claims so vague or so revised that nothing could ever count against them.

2b. Pseudoscience signatures.

Signature	Pattern
Unfalsifiability	No observation could disconfirm
Vagueness as defence	Any outcome confirms
Ad hoc rescue	New auxiliary added on every failure
Moving goalposts	Confirmation criterion shifts after evidence
Confirmation only	Only confirming cases counted
No risky predictions	Only "explains" already-known data
Reverse onus	Demands opponents disprove rather than supplying positive evidence
Conspiracy immunity	Counter-evidence reframed as cover-up
Authority-only support	"Studies show" / "experts agree" without method or citation
Galileo gambit	Persecution treated as evidence of correctness

2c. Unfalsifiable ≠ meaningless. Mathematical truths, definitions, value judgements, metaphysical claims can be coherent and important. The error is presenting them as if they were empirical facts.

---

Phase 3 — Evidence Quality

3a. Tier (rough, domain-translated).

T1  Multiple independent replications + meta-analysis + mechanism
T2  Single well-designed pre-registered RCT / strong natural experiment
T3  Cohort / longitudinal observational
T4  Case-control / cross-sectional
T5  Case series / single case reports
T6  Expert opinion (informative, not evidential alone)
T7  Mechanistic / animal / in-vitro (plausibility, not human effect)
T8  Anecdote, testimonial

3b. GRADE adjustments. Downgrade for: risk of bias, inconsistency, indirectness, imprecision, publication bias, no pre-registration, single study, fragility. Upgrade for: reproduced analyses, independent replication, large effect, dose–response, confounders that would all bias against, mechanistic coherence, pre-registration, open data/code/materials sufficient for checking.

Do not upgrade for peer-review status, journal impact, publisher reputation, author prestige, or citation count. Treat those as provenance/search signals that may justify checking the paper sooner; they do not add scientific warrant unless the underlying methods and results survive inspection.

After adjustment, certainty: High / Moderate / Low / Very Low. State which, and why.

3c. Statistical red flags.

Pattern	Concern
p reported, effect size missing	Statistical ≠ practical significance
p just under 0.05	Possible p-hacking
No CI	No precision info
CI includes null	"Significant" claim misleading
Multiple comparisons, no correction	Inflated false-positive rate
HARKing signs	Hypothesis suspiciously well-fitted to result
Subgroup analysis foregrounded	Often post-hoc fishing
Underpowered null claim	Absence of evidence ≠ evidence of absence
Relative risk without absolute	Inflates apparent importance
Absolute risk absent	Cannot judge practical significance
Odds / hazard / rate ratio presented as risk ratio	Effect metric substitution
Percent change without baseline	Magnitude laundering
Surrogate treated as hard outcome	Proxy ≠ thing of interest
Cohort selected on dependent variable	Selection effect as finding
Base rate ignored	Conditional probabilities misread

3c-1. Quantified effect decomposition. For any claim expressed as a percentage, rate, ratio, risk, hazard, odds, increase, decrease, benefit, or harm, write the decomposition before classification:

Population/timeframe: [who, where, duration]
Counts: [events / total] in each group
Baseline risk: [control/comparator event rate]
Comparison risk: [treatment/exposure event rate]
Absolute effect: [risk difference, ARR/ARI where applicable]
Relative effect: [RR/RRR/relative increase/OR/HR/rate ratio, named exactly]
Uncertainty: [CI/credible interval/p-value if available]
Practical meaning: [NNT/NNH when meaningful, or why not]

Never treat relative risk reduction as absolute risk reduction. Never translate odds ratios, hazard ratios, or rate ratios into risk ratios unless the conversion is justified from the underlying data. If the absolute effect cannot be recovered, classify the claim no stronger than provisional and flag missing denominator/baseline.

3d. Reproduction and replication. Single-study findings are provisional by default. Separate:

• Reproduction: same data/code/materials re-run or re-analysed and the reported results are recovered.
• Replication: independent data, samples, labs, populations, instruments, or teams test the same claim.

State: reproduced / not reproducible / replication attempted / replicated / single-study / mixed / failed / field has known crisis (psychology, nutrition epidemiology, preclinical cancer). Discount accordingly. A peer-reviewed but unreproduced paper remains provisional; a reproducible preprint may carry more scientific warrant than a prestigious unreproducible publication.

---

Phase 4 — Causal Inference

Correlation is data; causation is an inference from data + model. State the model.

4a. Bradford Hill viewpoints (considerations, not checkboxes). Stronger causal claims satisfy more.

Viewpoint	Check
Strength	Larger associations harder to explain by confounding
Consistency	Replicated across populations, methods, times
Temporality	Cause precedes effect; check reverse causation
Plausibility / mechanism	Known pathway
Coherence	Fits broader knowledge
Experiment	Intervention changes outcome (strongest)
Dose–response	More exposure → more outcome
Specificity	Often absent for real causes; prefer negative-control/falsification exposures
Analogy	Similar causes elsewhere → similar effects (weakest)

4a-1. Causal direction burden. If a claim aims to establish causation, explicitly address reverse causation before accepting causal language. State whether reverse causation was:

• Ruled out by design — randomisation, intervention, prospective measurement, temporal ordering, or another design feature makes reverse direction structurally implausible.
• Tested directly — lagged models, negative controls, sensitivity analyses, cross-lagged designs, intervention/removal tests, or equivalent checks evaluate the reverse direction.
• Made implausible — mechanism and timing make reverse direction unlikely enough for the strength of the causal claim.
• Unresolved — reverse direction remains plausible or untested.

If reverse causation is unresolved, downgrade to association, prediction, correlation, or hypothesis. Strong causal wording is not warranted when the reverse direction could explain the result.

4b. DAG / counterfactual. Diagram causal structure; identify confounders, mediators, colliders. Confounder = common cause of A and B. Adjusting for mediator hides effect; for confounder reveals it; for collider creates spurious correlation. Rule out: reverse causation, selection, sampling bias. Use negative controls — exposures or outcomes that should not show effect; if they do, suspect bias.

4c. Common errors. Correlation→causation · post hoc · ignored confounding · ecological fallacy (group → individuals) · individual fallacy (the reverse) · single-cause · Texas sharpshooter · survivorship.

---

Phase 5 — Bayesian Weighing

P(H | E) ∝ P(E | H) × P(H)
posterior ∝ likelihood × prior

What matters is the LIKELIHOOD RATIO  P(E | H) / P(E | ¬H),
not P(E | H) alone. Evidence that fits H but also fits ¬H is weak.

Discipline: state prior · state alternatives (at minimum: chance, bias, confounding, publication artefact) · compare likelihoods · update conservatively. Weak likelihood ratio applied to strong prior moves credence only a little.

"Extraordinary claims require extraordinary evidence" is this principle, not a rhetorical dismissal. The standard is symmetric: overturning a reproduced evidence base requires strong evidence; overturning a mere consensus mechanism requires auditing the political/institutional chain and tracing the underlying evidence.

---

Phase 6 — Source & Methodological Quality

6.0 — Source status vs. argument validity. Source status and CoI demotion set how much independent corroboration a claim requires before it is load-bearing. They do not determine whether the argument itself is valid. Peer review, journal venue, author seniority, institution, publisher, and citation count are status/provenance facts, not scientific principles. A Nobel-laureate book foreword by an outside-field author is low provenance and demands corroboration to load-bear; the arguments inside it are still answered by domain-expert, methodologically sound, reproducible evidence (substance refutation), not by listing the author's other contested beliefs. If you find yourself surfacing the arguer's unrelated views as part of the response to their argument, you have crossed from source-status discipline into Argument/Advocate Conflation (investigative-reasoning Phase 8). The moves look similar; they are not the same.

6a. Transparency and reproduction markers. Pre-registration · open data and code · executable analysis environment · blinded outcome assessment · random assignment · adequate power · attempted reproduction · attempted replication · disclosed COIs and funding · reproducible methods · null results reported.

6b. Conflict of interest. Funding/affiliation does not invalidate findings, but shifts the burden of proof for findings aligning with funder's commercial or ideological interest. Check: who funded, who employs authors, funder control of design/analysis/publication, independent replication by unaligned groups.

6c. Consensus failure modes (Phase 0g). A consensus claim never earns scientific weight by itself. It can be used only as a political/social prior when: authorities genuinely expert in relevant subdomain · claim within their scope · field tracks matters of fact · underlying evidence publicly available and reproducible · dissenters engaged on the merits.

Failure mode	Pattern
Funder capture	Funding concentrated in parties with stake in the answer
Ideological capture	Conclusions track political alignment more than evidence
Prestige cascade	High-status early claim entrenched before scrutiny
Citation cartel	Same small group cites itself; dissenters not cited
Publication bias	Null and contrary results unpublished
Manufactured dissent	Industry-funded fake controversy (inverse failure)
Silent disagreement	Apparent agreement masks private dissent where speaking is costly
Replication-crisis field	Apparent consensus rests on findings that don't replicate
Definitional drift	"X is established" but X has been redefined since the original evidence

6d. Evidence trace.

P1   Independently replicated + reproduced + transparent primary evidence
P2   Reproduced primary evidence, no independent replication yet
P3   Pre-registered primary evidence with auditable data/code/materials
P4   Systematic review / meta-analysis with reproducible search, risk-of-bias handling, and publication-bias checks
P5   Primary evidence not yet reproduced, including peer-reviewed papers and preprints
P6   Government / regulatory technical reports with inspectable methods
P7   Position statements of major scientific bodies
P8   Textbooks (lag the frontier)
P9   Science journalism citing primary sources, including reputable outlets
P10  General journalism / popular books
P11  Blogs, opinion media, advocacy sites
P12  Social media, anonymous sources, single testimonials

Trace claims toward P1. Peer review and venue reputation affect where to look, not how much warrant the result gets after inspection. Many disputes evaporate at the trace step.

Multi-source reconciliation. When ≥3 sources make conflicting claims about a single load-bearing fact, hand the dispute to investigative-reasoning Phase 3i (Source Triangulation) before classifying. The triangulation procedure exposes single-origin amplification ("10 outlets" with one source-node) and independent convergence (small numerical count but distinct nodes), both of which change a claim's classification more than the per-source quality ratings do.

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Phase 7 — Confidence Classification

Each claim gets a label on two axes: evidence strength + warrant type (Phase 0g). Both travel with the claim.

7a. Evidence-strength labels.

Label	Definition
Established fact	Multiple independent lines converge; predictive power demonstrated; replicated across labs/populations; mechanism understood; no remaining serious alternative
Well-supported finding	High-quality, replicated, mechanism plausible/known; small probability of revision
Provisionally accepted	Single high-quality study or limited replication; plausible mechanism; warrants caveat
Contested	High-quality evidence on both sides; field has not converged
Weak / preliminary	Suggestive but methodology limited, sample small, replication absent/mixed
Conjecture	Plausible, consistent with known, not yet specifically tested
Load-bearing assumption	Treated as true in service of argument but not itself supported
Opinion / value claim	Not empirically truth-apt
Unfalsifiable	No observation could disconfirm; may be coherent and important, but not a scientific fact
Likely false	Weight of high-quality evidence runs against
Refuted	Decisively disconfirmed; mechanism shown wrong or predictions decisively failed

7b. Warrant qualifier (attach to every empirical label — canonical project labels per CLAUDE.md / AGENTS.md):

• (traced) — followed evidence chain to a primary source fetched this session (URL + access date stated)
• (deferred to consensus) — relying on a named social/institutional consensus mechanism; name the body and state that it is not scientific warrant unless traced to reproduced or replicated evidence
• (deferred, fragile) — deferred but Phase 6c failure modes apply (funder capture, ideological capture, prestige cascade, replication crisis, etc.); state which
• (memory — unverified) — recalled from training data, not verified this session; never load-bearing without an explicit "this could be wrong" caveat
• (user-supplied — unverified) — supplied by the requester and not verified in-session; use only as a hypothesis to test
• (mixed) — part traced, part deferred; state which is which

7c. Consensus-specific labels (when a prior comes from the consensus mechanism rather than evidence the reader can follow):

Label	When
Consensus-prior	Named institutional consensus that appears tied to reproducible evidence; not traced; treat as a political/social prior, not scientific fact
Consensus-prior, fragile	Consensus exists but 6c failure modes present
Authority-warranted only	Single authority, no evidence trace
Orphaned	Cited as established, trace dead-ends — primary source missing, doesn't say what reported, or retracted

7d. Reporting. State strength + warrant + evidence (or consensus mechanism) that drove placement + what would change it. Examples:

• Established fact (traced) — multiple independent lines, mechanism known, chain followed
• Provisionally accepted (deferred to consensus) — field consensus appears tied to reproducible evidence; not traced; political/social prior only
• Provisionally accepted (deferred, consensus failure modes present) — consensus in replication-crisis field
• Authority-warranted only — single regulatory body asserts; no trace

The point: stop the user confusing "what the field believes" with "what the evidence shows". When they align, both labels say so. When they diverge — and historically they often have — the user is told.

7e. Consequential phrasing (mandatory for load-bearing claims). For each load-bearing classification, write the sentence the reader should use about this claim — including its hedges. The label is internal scaffolding for the analyst. The sentence is what the reader takes downstream. Without it, calibrated labels routinely degrade into binary "fact / not-fact" reception.

Pattern:

"X is [most plausible candidate / well-supported / contested / refuted] for Y, based on [evidence type]. Direct demonstration is [available / unavailable / partial] because [reason]. Inference is therefore [strong-but-defeasible / contested / unsafe to act on / decisive]."

Examples:

• ✗ "Smoking causes lung cancer." → ✓ "Smoking is a well-supported cause of lung cancer (multiple independent lines, dose-response, mechanism known); the inference is decisive."
• ✗ "SARS-CoV-2 causes COVID-19." → ✓ "SARS-CoV-2 is the most plausible causal candidate for COVID-19, based on sequence association, cell-culture cytopathic effect, animal-model challenge, and intervention data. Direct Koch-grade transmission demonstration in humans is unavailable (ethically impossible); inference is strong-but-defeasible."
• ✗ "Vitamin D supplementation reduces COVID mortality." → ✓ "Vitamin D supplementation is provisionally associated with reduced COVID mortality in observational and small-RCT data; large RCTs are mixed; inference is unsafe for clinical recommendation outside deficiency states."

If the calibrated sentence is too cumbersome to write, the classification is hiding a more honest qualifier and should be reconsidered. The cumbersomeness is the calibration.

---

Output

# Claim Classification: [source]

## Summary
- **Source:** [paper / article / claim set under audit]
- **Domain & standards applied:** [Phase 0e — which field's bar]
- **Verdict (bottom line):** [one-line — what the reader can rely on, what warrants scepticism]

## Per-Claim Audit

### C1: [short label]
- **Type:** [Phase 1]
- **Demarcation:** [falsifiable? pseudoscience signatures?]
- **Evidence:** tier + GRADE adjustments + replication + statistical flags
- **Causal status (if relevant):** Bradford Hill summary; reverse-causation status; alternatives ruled out
- **Bayesian context:** prior, strongest alternative, likelihood ratio direction
- **Source quality:** provenance tier, transparency, COIs
- **Warrant type:** traced / deferred / deferred with failure modes / mixed
- **Classification:** [Phase 7 strength + warrant qualifier]
- **Consequential sentence (Phase 7e):** [the calibrated sentence the reader should use — including hedges]
- **What would change this:**

[repeat per claim]

## Cross-Claim Issues
[Contradictions; dependencies; shared load-bearing assumptions]

## Source's Epistemic Profile
[Does the source distinguish facts from opinions, calibrate confidence, engage the strongest counter-evidence?]

## Confidence & Severity
- **Counts by classification label:** Established / Well-supported / Provisional / Contested / Weak / Conjecture / Assumption / Opinion / Unfalsifiable / Likely-false / Refuted
- **Warrant mix:** traced vs deferred vs deferred-fragile
- **Overall epistemic status of the source:**

## What Would Change This
[Per-claim falsification criteria collated; specific consensus-failure-mode evidence that would shift deferred classifications]

## Sources & Warrants
| Claim | Source | URL | Access date | Publication date | Warrant | Funding / ownership / mandate / alignment |
|---|---|---|---|---|---|---|

## Self-Audit
- **Symmetry test:** Would I have reached the same verdict if the politically/socially expected answer ran the other way? If no — explain. If you can't tell — say so. State explicitly per contested claim.
- **Cross-domain consistency:** Same standards applied across domains (Phase 0e), not mismatched (e.g. holding history to physics's bar or vice versa).
- **Consensus-mechanism audit:** Where warrant is `(deferred to consensus)`, were the Phase 6c failure modes actually checked, or assumed absent?

## Limits of This Analysis
[Claims left unchecked; primary sources not fetched; domain-expertise gaps; consensus mechanisms relied on but not audited]

---

Quick Reference

Pattern	Suspect	Default move
"Studies show…" (no citation)	Anonymous authority	Provenance unknown — investigate
"Scientists agree…" (no method)	Bare appeal to consensus	Treat as political/social prior only; warrant = deferred
"It's been proven that…"	Overstated certainty	Almost never literally true in science
"The science is settled"	Closing-down move	Reopen — settled by what, traced or deferred?
"Just a theory"	Folk-vs-scientific equivocation	Category error
One study found…	Single-study fallacy	Provisional at best
"Linked to" / "associated with" + strong conclusion	Correlation as causation	Apply Phase 4
Causal claim, reverse causation unaddressed	Directionality failure	Downgrade to association unless design/analysis rules out reverse direction
"Risk doubles" without absolute risk	Relative-risk inflation	Recompute as absolute
"50% reduction" without baseline risk	Relative-risk laundering	Require event rates and ARR/RRR separately
Odds ratio / hazard ratio described as "risk"	Metric substitution	Rename the metric or convert with data
Replication in crisis fields	Replication artefact	Discount; flag consensus as fragile
Cited expert speaking outside field	Misapplied authority	Discount
Single anomalous study contradicts large literature	Likely fluke	Default to body — but check whether body is deferred
Pre-registered + reproduced + independently replicated + open data/code	Genuine corroboration	Upgrade
Peer-reviewed / high-impact venue only	Status laundering	Do not upgrade; inspect reproduction, replication, and methods
Reproducible preprint with open data/code	Inspectable primary evidence	May outrank unreproduced peer-reviewed papers
Mechanism specified and tested	Independent line of support	Upgrade
Modal smuggling ("could", "may" → strong conclusion)	Hedge becoming claim	Match conclusion to evidence
Definitions shift mid-argument	Equivocation	Hold definition fixed
Field heavily funded by interested parties	Funder capture risk	Treat consensus as fragile
Trace dead-ends or contradicts citation	Orphaned claim	Label "orphaned"; do not propagate