Assessment and recommendations
Bibliographic record
Abstract
This OECD report comes at a time when Canada and the world is facing the COVID-19 pandemic crisis, which represents a source of great economic uncertainty.Due to confinement measures necessary to protect public health, there has been a shock to the Canadian labour market. For example, following a drop of over 1 million in March 2020, employment fell by about 2 million in April 2020. The magnitude of the decline in employment from February to April (-15.7%) far exceeds declines observed in previous labour market downturns. At the same time, Employment Insurance (EI) claims have soared, with 2 million claims over the last two weeks of March alone. COVID-19 restrictions have started to gradually ease as of May 2020 and have been followed by an initial rebound in employment, but it remains to be seen how the economic recovery will unfold. After rising 5.2 percentage points in April to 13.0%, the unemployment rate in Canada has continued to increase in May, hitting an all-time high of 13.7%.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".