Review of Best Practices in Labour Market Forecasting with an Application to the Canadian Aboriginal Population
Bibliographic record
Abstract
The Friendship Centres in Canada play a pivotal role in community and economic development by providing training and employment opportunities, facilitating social development, and building human and resource capacity for Aboriginal Canadians. The availability of occupational projections may facilitate the work of the Friendship Centres by providing valuable information concerning future labour market outcomes, allowing their programs to more appropriately prepare Aboriginal Canadians with the required skills, training and education to meet expected labour demand. By surveying the best practices in labour market demand and supply modeling used by national, sub-national and sectoral organizations, this report will help provide a stronger understanding of the potential power of labour market forecasting, while acknowledging the difficulties and obstacles inherent in any projection process. Furthermore, this report will discuss methodologies that could be implemented by the Friendship Centres to estimate the prospective occupational labour supply and demand facing Aboriginal Canadians.
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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 | 0.030 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".