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Record W3121828879

Evaluation par les nouveaux immigrants de leur vie au Canada

2010· article· fr· W3121828879 on OpenAlexaboutno aff
Grant Schellenberg, René Houle

Bibliographic record

VenueDirection des etudes analytiques : documents de recherche · 2010
Typearticle
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Le present article, fonde sur l'Enquete longitudinale aupres des immigrants du Canada (ELIC), analyse l'evaluation subjective que font de leur vie au Canada les nouveaux immigrants de la cohorte de 2000-2001. Cette etude offre un complement utile a d'autres etudes sur la situation des immigrants, etudes qui se concentrent souvent sur l'emploi, le revenu ou la sante. Quatre ans apres leur arrivee au pays, environ les trois quarts des repondants de l'ELIC se sont dits satisfaits ou tres satisfaits de leur vie au Canada, et une proportion comparable de repondants ont indique que leur vie au Canada est a la hauteur de leurs attentes ou les depasse. Pres de 9 repondants sur 10 ont affirme que, s'ils avaient a choisir de nouveau, ils prendraient encore la decision d'immigrer au Canada. Les evaluations subjectives sont associees a un large eventail de caracteristiques demographiques, sociales et economiques. Les evaluations positives de la vie au Canada sont moins frequentes chez les repondants dans la trentaine et dans la quarantaine, les diplomes universitaires et les demandeurs principaux de la categorie des travailleurs qualifies qu'elles ne le sont dans d'autres groupes. Si l'evaluation de la vie au Canada est correlee avec divers facteurs economiques comme le revenu personnel, elle l'est egalement avec des facteurs sociaux comme les relations avec les voisins et les perceptions en matiere de discrimination.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
GPT teacher head0.527
Teacher spread0.214 · 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 designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

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