MétaCan
Menu
Back to cohort
Record W2770222905 · doi:10.7202/1034562ar

Pourquoi le mouvement pour la paix ne rallie-t-il pas tout le monde ?

2016· article· fr· W2770222905 on OpenAlexaffvenueabout
Metta Spencer, Yves Prescott

Bibliographic record

VenueInternational Review of Community Development · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsMontreal Police ServiceUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Quelles sont les raisons qui expliquent pourquoi le mouvement pour la paix ne recrute pas tout le monde et comment se fait-il que la question du désarmement ne soit pas, au Canada, une priorité politique ? Selon l’auteure, la complexité des questions soulevées par le problème de la course aux armements et, en corollaire, l’insuffisance des repères partiels, en l’absence de référence idéologique permettant de saisir les événements dans leur ensemble, pourraient expliquer la situation. Les intellectuels sont ici invités à construire cette vision du monde cohérente et nécessaire pour encourager une participation élargie aux mouvements pour la paix.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.026
Scholarly communication0.0110.011
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0170.002

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 head0.053
GPT teacher head0.342
Teacher spread0.289 · 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 designQualitative
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
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueInternational Review of Community DevelopmentSame topicPeacebuilding and International SecurityFrench-language works237,207