Benchmarks for assessing labour market health: 2025 update
Bibliographic record
Abstract
This staff analytical note builds on Ens et al. (2021); Ens et al. (2022); Ens, See and Luu (2023); and Ens et al. (2024) to assess the health of the Canadian labour market. These earlier works established a framework for assessing the labour market given its diverse and segmented nature. This note includes the following: We provide an update on the state of the labour market, which has moved into modest excess supply since the 2024 update. We analyze two special topics that are important to understanding current and near-term labour market conditions: We perform an in-depth analysis to better understand how recent changes to US trade policy may affect different parts of the labour market. We explore the recent divergence of wage growth from other indicators and offer possible explanations for why wage growth remains above benchmark ranges despite the broader easing of the labour market. We update the range of benchmarks in our dashboard of indicators to ensure they remain relevant. To this end, we: add an additional year of data update the suite of wage indicators refine the calculation of the Hodrick-Prescott (HP) filter for wage growth measures
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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 | 0.027 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".