"Starting from another side, the bottom": volunteer work as a transition into the labour market for immigrant professionals
Bibliographic record
Abstract
In the past two decades, advanced capitalist countries have seen sustained growth in labour market participation along with a growth in the number of jobs workers tend to have in their working lives. Over a slightly longer period we also see that participation in both formal educational attainment and a range of non-compulsory learning/training has grown. However, labour market discrimination based on gender, age, disability and race/ethnicity remains a serious issue in virtually all OECD countries. ‘Challenging Transitions in Learning and Work’ presents a critical and expansive exploration of learning and work transitions within this context. These transitions are challenging for those enmeshed in them and need to be actively challenged through the critical research reported. The impetus for this volume, its conceptual framing, and much of the research emerges from the team of Canadian researchers who together completed case study and survey projects within the ‘Work and Lifelong Learning’ (WALL) network. The authors include leading scholars with established international reputations as well as emerging researchers with fresh perspectives. This volume will appeal to researchers and policy-makers internationally with an interest in educational studies and industrial sociology.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".