Bibliographic record
Abstract
Historically, in Canada, labour law has not only failed to protect Indigenous working people from the exclusionary logic of (colonization-induced) market capitalism but also marginalized (peripheral and vulnerable) working people among the settlers. In spite of the Canadian judiciary’s attempt to articulate a broader vision of labour law through the values of justice, liberty, equity, and participatory democracy, the foundational private contractual rationale of labour law acts as a constraint on the judiciary’s ability to develop a broader – and more inclusive – regulatory justification. In this article, I suggest that this tension between expansive normative values and narrow (exclusionary) regulatory justification of labour law could be usefully addressed by employing the idea of reconciliation, originally conceived to fashion the relationship between Indigenous and non-Indigenous peoples in Canada. I argue that an appropriately formulated idea of reconciliation should be able to promote a more inclusive conceptual foundation of labour law, one that is receptive of non-Eurocentric world-views in its foundational narrative and democratic in its continued execution. In so aiding labour law, the reconciliation perspective can close the gap between normative values and regulatory justification of the discipline.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.082 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".