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Record W2526240034

Impact des facteurs subjectifs sur les stratégies d'apprentissages des immigrants roumains qualifiés qui vivent un déclassement professionnel

2009· article· fr· W2526240034 on OpenAlexaboutno aff
Dorina Arbone

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesImmigrationArtLaw
DOInot available

Abstract

fetched live from OpenAlex

Malgre des niveaux de scolarite eleves, bon nombre d’immigrants eprouvent des difficultes a trouver un emploi selon leurs diplomes (Statistiques Canada, 2005). A un premier regard, il pourrait sembler que les communautes d’origine europeenne se heurtent a des obstacles moindres que d’autres communautes. L’exemple de la communaute roumaine prouve, au contraire, que les difficultes d’integration sont comparables. Afin de mieux comprendre ces difficultes d’integration, cette recherche analyse l’impact de differents facteurs subjectifs sur les strategies d’adaptation socioprofessionnelle des immigrants qui vivent un declassement professionnel. Ainsi, l’adoption d’une methodologie quantitative a ete necessaire pour repondre a la question de recherche. Appuyee sur un cadre theorique qui inclut les concepts de privation relative, d’estime de soi et de strategies d’acculturation, cette etude a ete menee aupres d’un echantillon constituee de 112 immigrants roumains de la region d’Ottawa-Gatineau. Les resultats obtenus illustrent l’impact de differents facteurs subjectifs sur les choix des strategies d’adaptation socioprofessionnelle. D’une part, l’immigrant roumain qui vit de la privation roumain qui a un bon niveau d’estime de soi augment sa confiance en soi; celle-ci semble l’aider a orienter ses efforts d’adaptation vers des apprentissages nouveaux.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.092
GPT teacher head0.403
Teacher spread0.310 · 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 teacher head, not a consensus.

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

Citations0
Published2009
Admission routes1
Has abstractyes

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Same topicHigher Education Learning PracticesFrench-language works237,207