Needs and presence of temporary migrant workers in the regions of Quebec (Canada): From public data to a unified database
Bibliographic record
Abstract
BackgroundDuring the recent years of labour shortage, many employers and employer associations have been calling for the recruitment of more temporary migrant workers (TMW) in the province of Quebec. The various programs, the needs expressed by employers, and the problems encountered by TMWs remain largely unknown to the structures that receive these workers (municipalities, Regional County Municipalities or RCM …) or help them (associations …). The real number of TMWs in each local territorial division (Administrative regions, RCM and municipalities) remains unknown. It is therefore difficult to develop welcoming policies or an inclusive framework that meets the needs of these populations.ObjectiveTo develop a more detailed knowledge of the presence of TMWs in Quebec and the needs expressed by employers.MethodsDesign of a unified database to elaborate a statistical portrait of the needs expressed by employers and the presence of TMWs in the province of Quebec by stream of the Temporary Foreign Worker Program (TFWP), by skill level, and by territory from 2017 to 2022. An exploratory data analysis to summarize the main characteristics of this longitudinal sample using statistical graphics and maps was conducted.ResultsThere are substantial differences between regions and their territories regarding the streams of the TFWP, occupations and skills needed. The data also illustrate the prominence of some companies who hire a large number of TMWs in the province of Quebec and the effect of the COVID-19 lockdowns.ConclusionOur analysis shows an increase of various types of TMWs in specific administrative regions and a gap in statistical data about workers from the TFWP for the organizing efforts of local environments (RCM, municipalities and their community sector) to respond to the needs of a growing number of these workers.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".