Educational goods for well-being in K-12 schools: foundational questions, student autonomy, and stratifications
Bibliographic record
Abstract
Student well-being has become a categorical focus for school education policy and practice across Canada and in many parts of the world. In the Manitoba context, student well-being is identified as a priority area by policymakers and educators in the public K-12 school system. Yet, there is a need for more clarity on the theoretical foundations that underpin notions of student well-being as well as how these conceptions translate into school programming across socio-political and geographic contexts. Both the conceptualization and implementation processes that address student well-being involve human (adult) values and choices about what to prioritize for students in schools. This dissertation consists of a series of three independent papers that explore the themes of distributive justice and educational goods for well-being in K-12 schools. The first paper, entitled, Three Foundational Questions for Policymakers and Practitioners Concerned with Student Well-being, explores three key questions that must be considered for any policymakers and practitioners concerned with student well-being in schools. The second paper, Reimagining Paternalism for a Well-being Mandate in K-12 School Education enquires into the importance of student autonomy when considering student well-being and makes the case for broadening student autonomy through a soft paternalism approach in schools. The final paper, entitled, Social Class and Access to Well-being Goods and Capabilities in K-12 Schools explores teachers’ perspectives, practices, and experiences in schools with student well-being. This qualitative research identifies how teachers characterize educational goods and capabilities for well-being in four different high school program settings across Winnipeg (Manitoba). Findings from this study demonstrate that healthy personal relationships are thought to be an important educational good for well-being, in addition to other goods such as personal fulfilment, personal autonomy, and democratic competence (in that order). Findings also reveal that educational goods for well-being appear to be differently stratified based on school program, which in turn, are stratified based on socioeconomic status as well as other factors.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".