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Record W2602188849 · doi:10.1163/18722636-12341364

Narcissism or Facts?

2017· article· en· W2602188849 on OpenAlexaff
Robert Piercey

Bibliographic record

VenueJournal of the Philosophy of History · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPragmatismEpistemologyPhilosophyNarrativeCriticismLiterature

Abstract

fetched live from OpenAlex

This essay asks whether a pragmatist philosophy of history can make sense of the notion of historical facts. It is tempting to think it cannot, since pragmatists insist, as James puts it, that the trail of the human serpent is over everything. Facts, by contrast, are typically thought of as something untouched by the human serpent, something impervious to what we think and do. I argue, however, that there is a way of understanding facts that is perfectly at home in pragmatist philosophy of history. Drawing on work by Robert Brandom, I propose that facts be interpreted inferentially. On this view, to call something a fact, or to say that the facts make my beliefs true, is simply a shorthand way of saying that a particular sort of relationship exists among certain sentences. I further show that this inferential understanding of facts is fully compatible with the distinctive features of historical inquiry. In particular, it is compatible with history’s irreducibly narrative character, and with the way different narratives can reveal radically different facts. Finally, I use this account of historical facts to respond to a classic criticism of pragmatism: the charge that pragmatism is narcissistic . I argue that pragmatism is narcissistic in only the minimal sense that it cannot countenance theory-neutral givens. But pragmatists can happily grant that there is more to truth than consensus, and that our claims are answerable to facts that everyone can get wrong.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.113
GPT teacher head0.265
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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