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Record W3137835047 · doi:10.1145/3262168

Session details: Semantics

2015· article· en· W3137835047 on OpenAlexaboutno aff
Michael Mislove

Bibliographic record

VenueACM SIGLOG News · 2015
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsnot available
Fundersnot available
KeywordsColumn (typography)Session (web analytics)Semantics (computer science)Component (thermodynamics)Foundation (evidence)Connection (principal bundle)Subject (documents)Computer scienceQuarter (Canadian coin)Mathematics educationMathematicsTheoretical computer scienceAlgebra over a fieldPure mathematicsProgramming languageWorld Wide WebPolitical scienceHistoryGeometry

Abstract

fetched live from OpenAlex

One of the goals of this column is to highlight breakthroughs so the semantics community can follow them as they unfold. This quarter's column concerns just such a breakthrough -- the amazing relationship between algebraic topology, a mainstay of mathematics, and type theory, a fundamental component of logic and computer science, that has emerged through Voevodsky's univalent foundation for mathematics. Working out the details of this connection was the subject of a recent year-long program at IAS that produced the HoTT book referenced in the column itself.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.799
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0120.008
Open science0.0010.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.7990.636

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 head0.056
GPT teacher head0.286
Teacher spread0.230 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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