Rhetorical Leadership in Framing a Supportive Social Climate for Educational Reforms Assisting Children with Disabilities
Bibliographic record
Abstract
is a remarkable woman in Canada named Anne Larcade. Ms. Larcade, mother of two, has a son with multiple disabilities. Alexandre always had academic trouble in school. Then at age 7, with no teaching assistant to watch out for him, he was abducted from a crowded school playground, tortured, and sexually abused. The incident exacerbated his problems, and its toll included the end of Larcade’s marriage, already strained by the demands of both parents holding professional careers while meeting the needs of a disabled child and his infant brother.1 In 1999, when Alexandre was 9, Larcade, now divorced, turned to the province for help in meeting the special requirements and sky-rocketing costs of her son’s care and education. Since the early 1980s, Ontario had had legislation requiring the province to sign special needs agreements to help support children whose needs were greater than could be met by their parents. So, Larcade was stunned to be told in August, 2000, that Children’s Aid Society could not pay for the services Alexandre required unless she signed a paper “legally abandoning ” him to become a ward of the state; her alternative was to let her son go without the services that would allow him to be re-integrated into the school system.2 Larcade learned that the special needs agreement law, designed to provide help 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".