Bibliographic record
Abstract
This article critically examines the Ontario government announcing in its 2007 budget that it would increase the minimum wage incrementally, the last hike to occur in March 2010. In March 2009, Premier McGuinty met with business leaders in a private, behind closed doors meeting. News of this leaked out revealing that he stated that he might cancel the remaining increases given economic conditions. Pressed by reporters to explain his apparent flip flop, and shamed by the lack of transparency, he reversed himself again saying this: When we talk about the minimum wage, we have to ask ourselves what it is that we owe both our workers and employers. I think clearly we owe them fairness. Our commitment was to get $10.25 an hour one year from now and we will honour that commitment. This article will review the procedural fairness issues arising in this scenario including both the legal definition and the political implications for democratic governance.
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 | 0.053 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.068 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".