Perspectives of School Leaders on Supporting Learners With Special Education Needs During the COVID-19 Pandemic: An Ethic of Care Analysis
Bibliographic record
Abstract
The ethic of care is a moral philosophy that has been used to describe and guide the work of educators, especially those working with students with special education needs (SEN). In this study, 36 principals and vice principals from four provinces in Canada were interviewed about their work with students with SEN during the pandemic. Responses were analyzed using the ethic of care framework. Accordingly, responses indicated that principals were particularly aware of, and responsive towards, the wide range of need experienced by students, their families, and school staff. Principals appeared especially concerned about the social needs of their students with SEN, the emotional support needs of the students' families, and the teachers' distress at not being able to meet all the educational needs of their students. Although most principals described the emotional toll of their work during the pandemic, none identified efforts directed towards self-care. This paper considers these findings in regard to motivational displacement as it relates to an ethic of care and calls for a broader consideration of need within education, such that support is extended to students, school staff and school leaders as the most effective means to foster healthy, future-ready schools. Key words: pandemic, principal, inclusive education, ethic of care, mental health.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".