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Record W4378473337 · doi:10.1111/phpr.12990

Philosophy's past: Cognitive values and the history of philosophy

2023· article· en· W4378473337 on OpenAlexaff
Phil Corkum

Bibliographic record

VenuePhilosophy and Phenomenological Research · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScholarshipEpistemologyNoveltyAnalogyValue (mathematics)IdeologySociologyPhilosophyPsychologyPoliticsLawSocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Recent authors hold that the role of historical scholarship within contemporary philosophical practice is to question current assumptions, to expose vestiges or to calibrate intuitions. On these views, historical scholarship is dispensable , since these roles can be achieved by nonhistorical methods. And the value of historical scholarship is contingent , since the need for the role depends on the presence of questionable assumptions, vestiges or comparable intuitions. In this paper I draw an analogy between scientific and philosophical practice, in order to float one role for historical scholarship that is nonreplicable and noncontingent. It has long been acknowledged that cognitive values – features of theories that facilitate understanding, such as ontological parsimony, ideological simplicity, computational ease and fecundity – play a key role within science. The role of some of these values within philosophy also has received attention but left understudied are the values of novelty and conservativeness. These values influence theory choice, the selection of methodology, the setting of research agenda, and the presentation of results; and are best assessed with a historically informed evaluation. This role for historical scholarship is not replicable by nonhistorical methods, and is not contingent on the presence of questionable assumptions, vestiges or comparable intuitions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.021
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.299
GPT teacher head0.333
Teacher spread0.034 · 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 designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

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