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Record W3049439922 · doi:10.1039/d0sc90150d

A diverse view of science to catalyse change

2020· article· en· W3049439922 on OpenAlexaff
César A. Urbina‐Blanco, Safia Z. Jilani, Isaiah R. Speight, Michael J. Bojdys, Tomislav Friščić, J. Fraser Stoddart, Toby L. Nelson, James Mack, Renã A. S. Robinson, Emanuel Waddell, Jodie L. Lutkenhaus, Murrell Godfrey, Martine I. Abboud, Stephen Opeyemi Aderinto, Damilola V. Aderohunmu, Lučka Bibič, João Borges, Vy M. Dong, Lori Ferrins, Fun Man Fung, Torsten John, Felicia Phei Lin Lim, Sarah L. Masters, Dickson Mambwe, Pall Thordarson, Maria‐Magdalena Titirici, Gabriela D. Tormet‐González, Miriam M. Unterlass, Austin Wadle, Vivian Wing‐Wah Yam, Ying‐Wei Yang

Bibliographic record

VenueChemical Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsStillwater (Canada)McGill University
Fundersnot available
KeywordsDiversity (politics)ExcellenceValue (mathematics)EpistemologySociologyEngineering ethicsEnvironmental ethicsComputer sciencePhilosophyEngineering

Abstract

fetched live from OpenAlex

Valuing diversity leads to scientific excellence, the progress of science and, most importantly, it is simply the right thing to do. We must value diversity not only in words, but also in actions.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.008
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.119
GPT teacher head0.345
Teacher spread0.226 · 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 designBench or experimental
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

Citations7
Published2020
Admission routes1
Has abstractyes

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