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Record W2118301195 · doi:10.1017/s0008423907070394

Villes, Régions et Universités : Recherches, Innovations et Territoires

2007· article· fr· W2118301195 on OpenAlexaffabout
Éric Champagne

Bibliographic record

VenueCanadian Journal of Political Science · 2007
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of OttawaInstitute on Governance
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Villes, Régions et Universités : Recherches, Innovations et Territoires., Sous la direction de Hudon, Raymond et Jean-Pierre Augustin,, Les Presses de l'Université Laval, Québec, 2005, 385 p. Ce livre est un compte rendu particulièrement fidèle des deuxièmes Rencontres Champlain-Montaigne entre la ville de Bordeaux en France et celle de Québec au Canada, qui se sont tenues en 2002. Ces rencontres se veulent un forum d'échanges portant sur les relations entre les universités et les partenaires socioéconomiques de leurs espaces régionaux respectifs. Elles ont été établies dans le contexte des relations bilatérales entre ces deux villes qui prévalent depuis leur jumelage en 1962. Depuis 2000, un protocole de coopération a été signé entre l'Université Laval, le Pôle universitaire de Bordeaux, la ville de Québec, la ville de Bordeaux, le Conseil régional de concertation et développement de la région de Québec et le Conseil régional d'Aquitaine. Les premières Rencontres Champlain-Montaigne se sont déroulées à Québec en 2001. Elles avaient déjà pour thème les villes, les régions et les universités et s'intéressaient tout particulièrement aux acteurs et à leurs pratiques. La seconde édition de ces rencontres les inscrit donc dans la durée et confirme l'intérêt pour le même sujet. Ces deuxièmes rencontres abordent toutefois la question du point de vue de la recherche, des innovations et de la dimension territoriale.

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)0.005
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.101
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.015
Science and technology studies0.0080.009
Scholarly communication0.0170.012
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0410.008

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 head0.102
GPT teacher head0.381
Teacher spread0.279 · 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
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

Citations0
Published2007
Admission routes2
Has abstractyes

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