Nurturing the next Canadian generation: The case for labour market research
Bibliographic record
Abstract
As rates of population and labour force growth slow in Canada, the country faces important challenges in promoting economic growth and sustaining prosperity. Among the most important public issues are increasing labour force participation rates among groups with low or declining rates of work and reforming education to better prepare graduates for the jobs of the new economy. At the same time, Canada needs to respond to the shifting geography of work. The concentration of employment in a limited number of major urban centres is driving young people to seek work in high-cost cities, while many smaller cities and regions face the prospect of economic and demographic decline.Alors que les taux de population et la croissance de la population active ralentissent au Canada, le pays devra relever d’importants défis pour promouvoir la croissance économique et maintenir la prospérité. Les plus importantes questions d’ordre public porteront, entre autres, sur le taux de participation, au sein de la population active, de groupes présentant des taux d’emploi faibles ou en déclin et la réforme de l’éducation afin de mieux préparer les diplômés aux emplois de la nouvelle économie. Le Canada doit, en même temps, aborder la géographie changeante du travail. La concentration des emplois dans quelques grands centres urbains pousse les jeunes à chercher du travail dans les villes où le coût est élevé, alors que les villes plus petites et les régions sont confrontées au déclin économique et démographique.Mots-clés : population et environnement; climat; utilisation d’énergie; pointe de population
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.030 | 0.035 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".