Bibliographic record
Abstract
The flexibility of labour markets has entailed a growing use of temporary employment that is associated with limited work arrangements that are often unprotected, poorly paid, and socio‐economically insecure. Yet, the factors that shape the patterning of temporary employment and its types (seasonal, casual, and contract jobs) are relatively unknown in Canada. Using data from Statistics Canada's 2016 Labour Force Survey and the 2016 Census, this paper examines the spatial characteristics influencing the patterns of temporary employment and its types across Canada's census metropolitan areas and census agglomerations (CMAs/CAs). We hypothesize that geographies characterized by a high proportion of immigrants, highly educated populations, and highly unemployed populations have a greater likelihood of workers employed in temporary employment and its types. Our analyses support these hypotheses as population characteristics (CMAs/CAs with a high share of Asian immigrants); labour market characteristics (unemployment rate and prevalence of low income in CMAs/CAs); and human capital characteristics (CMAs/CAs characterized by a large share of populations with a bachelor's degree or higher) contributed more to explaining positive temporary employment outcomes than did occupation characteristics. This study adds valuable insights into the spatial dimensions of labour market insecurity that could be valuable in formulating place‐based policies that address labour market inequalities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".