What Does Mental Health Mean? An Ecocritical Conceptual Review of an Albertan Curricula
Bibliographic record
Abstract
What does mental health mean?— In a review undertaken to respond to this question for my doctoral research, I have found in common psychology, psychiatry, and public health, a focus of study for exploring the origins of conceptualizations given to mental health. Yet, curiously, inquiries that questioned curricular inheritances or the ideological foundations of mental health as a concept did not appear as persistently within curriculum studies, a discipline invested in understanding the influences of affective-social ecologies on knowledge construction. As an educator who has supported diverse urban public schools with mental health initiatives on Treaty 6 Territory, I found a lack of research on mental health in curriculum studies a troublesome condition. So, I delved deeper into a further review, using sources closer to home. This time, I would apply an ecocritical hermeneutic research approach. Soon, I would learn of a legacy founded through a mental hygiene movement entrenched in a settler-colonial worldview generating pioneering metaphors to elicit public support for eugenics as a project of re-place-ment. It seems that beliefs about mental health have been quite harmful here in Alberta when reinforced by governing ideological assumptions rooted within overt, subtle, and forgotten, expressions of meaning and acts to establish the normative.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".