‘Access without Fear!’: Reconceptualizing ‘Access’ to Schooling for Undocumented Students in Toronto
Bibliographic record
Abstract
This paper uses Chela Sandoval’s (2000) concept of meta-ideologizing to examine how definitions of ‘access’ are reframed to further the goals of social justice activists. Meta-ideologizing refers to re-operationalizing liberal, widely-accepted terms to fit the needs of a community. The paper draws from 14 semi-structured interviews with individuals pivotal to the passing and implementation of Toronto’s ‘Students Without Legal Immigration Status Policy’, also known as a ‘Don’t Ask, Don’t Tell’ policy. It also employs data from literature developed by stakeholders as well as the author’s experiential knowledge. It examines how organizers have reframed the concept of ‘access’ by extending its focus beyond entry into schools and including the need for undocumented migrants to be safe and have access to other social services. It also analyzes the ways bureaucratic logic can invisibilize the gains made by developing procedures that reify illegalization.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.042 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".