The coin model of privilege and critical allyship: implications for health
Bibliographic record
Abstract
Health inequities are widespread and persistent, and the root causes are social, political and economic as opposed to exclusively behavioural or genetic. A barrier to transformative change is the tendency to frame these inequities as unfair consequences of social structures that result in disadvantage, without also considering how these same structures give unearned advantage, or privilege, to others. Eclipsing privilege in discussions of health equity is a crucial shortcoming, because how one frames the problem sets the range of possible solutions that will follow. If inequity is framed exclusively as a problem facing people who are disadvantaged, then responses will only ever target the needs of these groups without redressing the social structures causing disadvantages. Furthermore, responses will ignore the complicity of the corollary groups who receive unearned and unfair advantage from these same structures. In other words, we are missing the bigger picture. In this conceptualization of health inequity, we have limited the potential for disruptive action to end these enduring patterns.The goal of this article is to advance understanding and action on health inequities and the social determinants of health by introducing a framework for transformative change: the Coin Model of Privilege and Critical Allyship. First, I introduce the model, which explains how social structures produce both unearned advantage and disadvantage. The model embraces an intersectional approach to understand how systems of inequality, such as sexism, racism and ableism, interact with each other to produce complex patterns of privilege and oppression. Second, I describe principles for practicing critical allyship to guide the actions of people in positions of privilege for resisting the unjust structures that produce health inequities. The article is a call to action for all working in health to (1) recognize their positions of privilege, and (2) use this understanding to reorient their approach from saving unfortunate people to working in solidarity and collective action on systems of inequality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".