What Do We Know? Reviewing the State of Knowledge on Climate, Work and Employment in Canada
Bibliographic record
Abstract
This paper presents the results of the first national ‘state of expert knowledge’ study of the impact of climate change on work and employment in Canada. Climate change is defined as recent changes in climate attributable to human activity. The What do we know? project, led by Lipsig-Mummé with Canadian academics, trade unionists and private sector labour market analysts, explores the state of knowledge about the complex interaction between climate change, response to climate change, and work and employment in Canada, in six economic sectors between 1995 and 2010. The sectors are: construction, energy, forestry, transportation equipment, postal services, and tourism. The paper begins by setting out the three international debates which shape the issue and its research. Second, it discusses its unusual research methodology. Third, the paper summarizes the research findings. Fourth, it identifies holes, silences, and next research questions on the climate/work relationship.
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 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.052 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".