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Record W4399764578 · doi:10.7202/1111516ar

[ Sans Titre - No Title ]Résumé du livre: Putting Skill to Work: How to Create Good Jobs in Uncertain Times, par Nichola Lowe. Cambridge, Massachusetts: The MIT Press, 2021. 200 pages. ISBN 9780262045162

2023· article· fr· W4399764578 on OpenAlexaffvenue
Sara Pérez‐Lauzon

Bibliographic record

VenueRelations industrielles · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsWork (physics)Operations researchManagementArtLibrary scienceComputer scienceHumanitiesSociologyMathematicsEngineeringEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

Résumé du livre: Putting Skill to Work: How to Create Good Jobs in Uncertain Times, par Nichola Lowe. Cambridge, Massachusetts: The MIT Press, 2021. 200 pages. ISBN 9780262045162. Un article de la revue Relations industrielles / Industrial Relations (La contribution des relations industrielles à la compréhension de l'avenir du travail et de l'emploi) diffusée par la plateforme Érudit.

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)0.001
metaresearch head score (Gemma)0.007
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: Review · Consensus signal: none
Teacher disagreement score0.386
Threshold uncertainty score0.875

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.007
Science and technology studies0.0020.002
Scholarly communication0.0080.009
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.3860.335

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 head0.024
GPT teacher head0.246
Teacher spread0.221 · 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
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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