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Record W2467690823 · doi:10.1080/09608788.2016.1177483

Ross’s place in the history of analytic philosophy

2016· article· en· W2467690823 on OpenAlexfundno aff
David Kaspar

Bibliographic record

VenueBritish Journal for the History of Philosophy · 2016
Typearticle
Languageen
FieldPsychology
TopicPhilosophy and Theoretical Science
Canadian institutionsnot available
FundersUniversité du Québec à Montréal
KeywordsPhilosophyAnalytic philosophyEpistemologyEnvironmental ethicsContemporary philosophy

Abstract

fetched live from OpenAlex

With the recent revival of moral intuitionism, the work of W. D. Ross has grown in stature. But if we look at some recent well-regarded histories, anthologies and companions of analytic philosophy, Ross is noticeably absent. This discrepancy of assessments raises the question of Ross’s place in the history of analytic philosophy. Hans-Johann Glock has recently claimed that Ross is not an analytic philosopher at all, but is instead a ‘traditional philosopher’. In this article, I will identify several undeniable features of analytic philosophy that Ross’s work bears: a focus on linguistic analysis, great respect for pre-theoretical thoughts, the conviction that philosophy is a collaborative, piecemeal enterprise and so on. Such an investigation, I claim, reveals two historically significant results: Ross was the first ethicist to fully draw from commonsense beliefs about morality in light of characteristic analytic considerations to secure his theory. Two, concerning the matter of whether the notions ‘right’ and ‘good’ are reducible to other notions, Ross appears to have been right: ‘right’ and ‘good’ are irreducible notions. The classical analytic metaethicists, who based their entire research programme on the promise of finding suitable reductive semantic analyses of ‘right’ and ‘good’, were wrong. These results, I believe, suffice to secure W. D. Ross a high place in the history of analytic philosophy.

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 categoriesnone
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 score0.964
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.055
GPT teacher head0.283
Teacher spread0.229 · 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
Published2016
Admission routes1
Has abstractyes

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