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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".