Toronto the Good? The Access T.O. Policy - Making Toronto a Sanctuary City
Bibliographic record
Abstract
This paper examines Toronto's Access T.O. policy, a policy created to transform Toronto into a sanctuary city. I argue that the Access T.O. policy has made progress towards turning Toronto into a practicable sanctuary city. However, I also highlight areas where the policy needs improvement and further expansion. I also show how the City of Toronto's Access T.O. policy offers an alternative approach to migration and settlement policies found at the level of the Canadian federal state and illustrate how these policies diverge and contradict. The Access T.O. policy, like other sanctuary cities, is shown to provide an alternative understanding and implementation of citizenship, belonging, rights, ethics and morality, human agency, security and borders to that found in federal state policies. The paper provides background information on sanctuary cities prior to entering this aforementioned discussion and concludes with considerations for Access T.O.'s continued expansion and implementation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".