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Record W2767952352 · doi:10.4000/books.pum.4287

Économie de l’innovation : innovation, croissance et productivité

2015· book-chapter· fr· W2767952352 on OpenAlexfundno aff
Petr Hanel

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2015
Typebook-chapter
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignOrganisation de Coopération et de Développement ÉconomiquesUniversity of TorontoJohns Hopkins UniversityPrinceton UniversityUniversity of OxfordHarvard UniversityUniversity of CambridgeWayne State UniversityState University of New YorkStanford Bio-XStyrelsen för Internationellt Utvecklingssamarbete
KeywordsTechnology innovationEconomicsIndustrial organization

Abstract

fetched live from OpenAlex

L’innovation est la force dynamique qui change l’économie en fournissant de nouveaux produits et procédés de production. La diffusion d’innovations améliore la productivité, condition nécessaire pour la croissance d’une économie au-delà des limites imposées par la disponibilité de la main-d’œuvre (voir Économie, science et technologie et Impacts économiques).L’innovation peut prendre trois formes : l’introduction sur le marché d’un produit

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.006
Scholarly communication0.0060.004
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.036
GPT teacher head0.195
Teacher spread0.159 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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