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Record W7028099573

(In)disciplines (In)disciplines

2017· other· en· W7028099573 on OpenAlexaboutno aff

Bibliographic record

VenueOpenEdition (OpenEdition) · 2017
Typeother
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Nova scotiaGeneral interest
DOInot available

Abstract

fetched live from OpenAlex

L'Association canadienne des sociologues et des anthropologues de langue française (ACSALF) organise son colloque général sur le thème des (in)disciplines.L'invitation s'adresse aux anthropologues et sociologues à ceux et celles qui partagent leurs perspectives d'enquête et d'analyse.Notre ambition est d'offrir une compréhension plus affinée des phénomènes qui transforment et façonnent les mondes contemporains, tout en explorant de nouvelles avenues en termes de conceptualisation et d'analyse. Modalités de soumission des propositions Communications individuellesSoumettre un résumé de 250 mots et de trois à cinq mots-clés.Le résumé doit être accompagné du nom de l'auteur, son affiliation (institution et département), son adresse courriel et ses coordonnées postales.Ateliers L'organisateur de l'atelier doit soumettre un résumé de 350 mots, trois à 5 mots-clés.Les résumés (250 mots) des communications de chacun des participants pourront être soumis au même moment ou à la suite de l'acceptation de l'atelier.Veuillez noter que les ateliers se composent de quatre ou cinq communications, en plus d'un président rapporteur, le cas échéant.Le résumé doit être accompagné du nom de l'organisateur et de ceux des participants (si connus), de leurs affiliations (institution et département), de leurs adresses courriel et leurs coordonnées postales.L'organisateur de l'atelier est responsable du recrutement des participants.

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)0.004
metaresearch head score (Gemma)0.011
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: Other · Consensus signal: Other
Teacher disagreement score0.196
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0040.006
Scholarly communication0.0160.008
Open science0.0020.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.1960.076

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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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