Ethnolinguistic Identity and Vocational Readiness as Non-Cognitive Factors Related to College Adaptation and Satisfaction with Life Among Franco-Ontarian Post-Secondary Students Living in an Anglo-Dominant Context
Bibliographic record
Abstract
The purpose of this research was to determine how Francophone Ethnolinguistic Identity and Vocational Readiness, facilitate the College Adaptation and predict students’ Satisfaction with Life. Using a sample of 179 first-year students, the development of a novel scale used to measure Vocational Readiness, or the degree to which a student is ready to make a post-secondary program choice and be engaged in the transition to post-secondary studies, was done and validated. Correlational analyses showed that all aspects of Vocational Readiness are consistently associated with better overall Adaptation in College and overall Vocational Readiness is associated with all subscales of College Adaptation. Regression analyses showed that Vocational Readiness was a significant predictor of College Adaptation and Satisfaction with Life Francophone Ethnolinguistic Identity was also a significant predictor of Satisfaction with Life. The interaction between Vocational Readiness and Francophone Ethnolinguistic Identity (Model F) was a significant, negative predictor of Satisfaction with Life. Post-hoc regression analyses indicated that both Vocational Readiness and Francophone Ethnolinguistic Identity are significant predictors of Social Adaptation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".