Turning the tide on inequity through systematic equity action-analysis
Bibliographic record
Abstract
BACKGROUND: Collective agreement about the importance of centering equity in health research, practice, and policy is growing. Yet, responsibility for advancing equity is often situated as belonging to a vague group of 'others', or delegated to the leadership of 'equity-seeking' or 'equity-deserving' groups who are tasked to lead systems transformation while simultaneously navigating the violence and harms of oppression within those same systems. Equity efforts also often overlook the breadth of equity scholarship. Harnessing the potential of current interests in advancing equity requires systematic, evidence-guided, theoretically rigorous ways for people to embrace their own agency and influence over the systems in which they are situated. ln this article, we introduce and describe the Systematic Equity Action-Analysis (SEA) Framework as a tool that translates equity scholarship and evidence into a structured process that leaders, teams, and communities can use to advance equity in their own settings. METHODS: This framework was derived through a dialogic, critically reflective and scholarly process of integrating methodological insights garnered over years of equity-centred research and practice. Each author, in a variety of ways, brought engaged equity perspectives to the dialogue, bringing practical and lived experience to conversation and writing. Our scholarly dialogue was grounded in critical and relational lenses, and involved synthesis of theory and practice from a broad range of applications and cases. RESULTS: The SEA Framework balances practices of agency, humility, critically reflective dialogue, and systems thinking. The framework guides users through four elements of analysis (worldview, coherence, potential, and accountability) to systematically interrogate how and where equity is integrated in a setting or object of action-analysis. Because equity issues are present in virtually all aspects of society, the kinds of 'things' the framework could be applied to is only limited by the imagination of its users. It can inform retrospective or prospective work, by groups external to a policy or practice setting (e.g., using public documents to assess a research funding policy landscape); or internal to a system, policy, or practice setting (e.g., faculty engaging in a critically reflective examination of equity in the undergraduate program they deliver). CONCLUSIONS: While not a panacea, this unique contribution to the science of health equity equips people to explicitly recognize and interrupt their own entanglements in the intersecting systems of oppression and injustice that produce and uphold inequities.
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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 | 0.212 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".