Ice Storm Experiences of Persons with Disabilities: Knowledge is Safety
Bibliographic record
Abstract
Abstract: Questionnaire responses of ice storm victims with and without disabilities were compared and 15 women with disabilities were interviewed about their experiences. Results are examined from the Social Model of Disability perspective in terms of dealing with unexpected environmental barriers to inclusion. Key Words: ice storm, disaster, social model January is one of the coldest months of the year in Eastern Canada. In January, 1998, however, Montreal had an unusual weather event. In less than 24 hours the city was covered with freezing rain. Its accumulation during this brief period caused a crisis. A considerable part of the population of Montreal and the southern portion of the province of Quebec was without electricity and heating for homes and offices. For some, this lasted up to three weeks. This incident became widely known as “the ice storm ” and it differed from other natural disasters in that it did not cause massive death or famine. Canadians who lived through the ice storm went back to their regular routines relatively quickly once weather conditions returned to normal. But the ice storm had dramatic effects on the everyday lives of those who experienced it. To examine these effects, in Study 1 we administered a battery of questionnaires to individuals with and without disabilities immediately after the ice storm. In Study 2, we examined unstructured interview responses of women with various disabilities. Some of these interviews involved retrospective recollections of the incident while others were obtained through informal conversations with women recorded during the 1998 ice storm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".