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Record W2075941278 · doi:10.1145/353171.353194

An Aristotelian understanding of object-oriented programming

2000· article· en· W2075941278 on OpenAlexaff
Derek Rayside, Gerard T. Campbell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsSt. Jerome's UniversityUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceObject (grammar)Logic programmingPredicate (mathematical logic)Object-oriented programmingProgramming languageFirst-order logicProgramming paradigmEpistemologyRelation (database)Artificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The folklore of the object-oriented programming community at times maintains that object-oriented programming has drawn inspiration from philosophy, specically that of Aristotle. We investigate this relation, first of all, in the hope of attaining a better understanding of object-oriented programming and, secondly, to explain aspects of Aristotelian logic to the computer science research community (since it differs from first order predicate calculus in a number of important ways). In both respects we endeavour to contribute to the theory of objects, albeit in a more philosophical than mathematical fashion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.022
Scholarly communication0.0080.014
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2000
Admission routes1
Has abstractyes

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