MétaCan
Menu
Back to cohort
Record W7144259848 · doi:10.34386/0000006895

カナダに移住した日本人女性の適応プロセス:支援ネットワークおよびコミュニティとの関係に焦点をあてて

2020· article· ja· W7144259848 on OpenAlexaboutno aff
Yumiko KAMISE

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2020
Typearticle
Languageja
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationResidenceOrder (exchange)Adaptation (eye)Language barrierFirst language

Abstract

fetched live from OpenAlex

This study focused on the adaptation processes of Japanese women who have immigrated to Canada. I interviewed 11 Japanese women, who ranged in age from their 30s to 50s, who have Permanent Residence (PR) in Canada. I asked them questions regarding their immigration background, the people who helped them, the language classes for immigrants funded by the Canadian government, and their attitudes toward their community. I found that during their initial stay, most of them built functional support networks, which helped them when they returned to Canada in order to start their new life as immigrants. Moreover, a Canadian husband’s support was not sufficient for a Japanese wife’s adaptation. They tended to feel a lack of social connections when starting their new lives. Farther, they did not utilize language programs for newcomers very much; however, the one who did take part in an English language program evaluated it very highly. Finally, most of the participants reported there was no active community in their residential area, like there had been in Japan. They received community news from emails, flyers, and notices at their condominiums.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.291
Teacher spread0.249 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInstitutional Repositories DataBase (IRDB)Same topicMigration, Ethnicity, and EconomyFrench-language works237,207