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Record W4403952721 · doi:10.1017/can.2024.30

Opportunity Costs and Resource Allocation Problems: Epistemology for Finite Minds

2023· article· en· W4403952721 on OpenAlexaff
Endre Begby

Bibliographic record

VenueCanadian Journal of Philosophy · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsResource allocationResource (disambiguation)EpistemologyEconomicsPhilosophyComputer scienceManagement

Abstract

fetched live from OpenAlex

Abstract Overwhelmingly, philosophers tend to work on the assumption that epistemic justification is a normative status that supervenes on the relation between a cognitive subject, some body of evidence, and a particular proposition (or “hypothesis”). This article will explore some motivations for moving in the direction of a rather different view. On this view, we are invited to think of the relevant epistemic norm(s) as applying more widely to the competent exercise of epistemic agency, where it is understood that cognitive subjects are simultaneously engaged in a number of different epistemic pursuits (distinct “lines of inquiry”), each placing irreconcilable demands on our limited cognitive resources. In effect, adopting this view would require shifting our normative epistemic concern away from the question of how a subject stands with respect to the evidence bearing on the hypothesis at stake in any one line of inquiry, and over onto the question of how well they cope with the inherent risks of epistemic resource management across several lines of inquiry. While this conclusion brings to light important connections between practical and epistemic rationality, it does not collapse the distinction between them. It does, however, constitute a step in the direction of a more systematically developed account of “non-ideal epistemology.”

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.123
GPT teacher head0.274
Teacher spread0.151 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2023
Admission routes1
Has abstractyes

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