An Intersectionality-Informed Model of Social Inclusion and Exclusion Gemma Hunting
Bibliographic record
Abstract
Social inclusion has increasingly been positioned within research and policy as integral to addressing stigma and discrimination related to mental health and substance use. Yet there is a lack of consensus about the meaning of social inclusion and how this concept can be applied to understand the broader social contexts that influence health and inequity. In this paper, we respond to a recently developed model of social inclusion for mental health and substance use in British Columbia (BC), Canada, by proposing an alternative model: an Intersectionality-Informed Model of Social Inclusion and Exclusion. Drawing on the BC model, we demonstrate what we see as key limitations of current conceptualizations of social inclusion and highlight the ways in which the proposed model extends, improves, and complicates understandings of social inclusion. We argue that this inquiry is a necessary precursor to better addressing the complexities of stigma, discrimination, and social exclusion, and in so doing, to promoting social inclusion and equity.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".