MétaCan
Menu
Back to cohort
Record W2948876103

Espace de creation et d'expression pour soutenir les jeunes ayant vecu l'exil

2019· article· fr· W2948876103 on OpenAlexaffabout
Audrey Lamothe-Lachaîne

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Le passage a l'adolescence, combinee a une situation migratoire comme l'exil, peut affecter le processus d'inclusion scolaire, a cause des exigences que suppose la superposition de ces deux types de changements. Devant ces defis auxquels s’ajoutent des obstacles linguistiques, les espaces d'expression sont limites pour les jeunes issus de l'immigration humanitaire, voire absents, meme s'ils peuvent contribuer a leur bien-etre emotionnel et social et, prendre part a notre comprehension de leurs parcours scolaires. Dans un cadre securisant, les activites visant le partage de leurs histoires tout en mobilisant une posture reflexive et creative par la realisation d’un projet peuvent soutenir leur transition dans un nouvel environnement scolaire et leur passage vers l’âge adulte. La communication portera sur un projet d’atelier participatif offrant des activites d’expression creatrice qui menaient a l’accomplissement d’un recit numerique personnel. L’atelier, hors scolaire, etait a offert a des jeunes ayant vecu l’exil, reinstalles depuis quelques annees au Quebec. La methodologie s’est basee sur des entretiens, groupes de discussion et les archives generees lors des activites. Nous allons plus particulierement se concentrer sur l’histoire de deux jeunes.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.035
GPT teacher head0.337
Teacher spread0.302 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicFrench Language Learning MethodsFrench-language works237,207