FLIPPING STIGMA ON ITS EAR: A TOOLKIT FROM PARTICIPATORY ACTION RESEARCH
Bibliographic record
Abstract
Abstract People living with dementia face persistent stigma, discrimination and social exclusion, with significant emotional, physical and social consequences. Addressing this requires changing attitudes and fostering actions for communities to include people with dementia as citizens with agency and self-determination. This presentation highlights the work of an Action Group (AG) of people living with dementia. As part of a four-year Participatory Action Research study aimed at addressing the stigma, discrimination and social exclusion that is so common to the dementia experience, members of the AG in partnership with the research team developed the Flipping Stigma on its Ear Toolkit. Focus will be on the action-oriented nature of this research project, an overview of the toolkit, and exploration of the communal space that was created by AG members in the process of working together, learning from one another, and making a collective contribution towards addressing stigma, discrimination and social exclusion.
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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 | 0.074 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".