Bibliographic record
Abstract
We are in an important technological moment in history, where experts in academia, research institutes, and non-governmental organizations posit that developments in artificial intelligence (ai) will lead to widespread disruptions in the labour market. This article addresses this claim by asking if organized labour sees ai as an equally imminent threat. Moreover, it asks how labour is preparing to challenge the power of capital as employers leverage automation in an age of neoliberal precarity. Online materials published by unions affiliated with the Canadian Labour Congress are reviewed here through discursive analysis. Our findings indicate that while no union has expressed opposition to technological change, many have questioned how employers leverage it in the workplace and its wider geopolitical and societal effects that affect their members and communities. We find that discussion around technological change emphasizes that technology makes work better and safer in a human-centred work environment. Overall, organized labour in Canada is attentive to issues within the political-economic context of automation, precarious work, community impacts, the role of government and regulation, skills and retraining, and job loss, among others. Given the view of technology held by organized labour, we challenge perspectives of both techno-pessimism and techno-optimism and highlight instead that labour unions are in a unique position to both respond and adapt to the evolution of work. Expanded strategic interventions around automation are needed to combat precarious work and the erosion of working conditions at present and in the coming decade(s), and we point to some notable efforts that are underway.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".