Action against inequalities: a synthesis of social justice & equity, diversity, inclusion frameworks
Bibliographic record
Abstract
Inequalities in health have long been recognized as interconnected with social, economic, and various other inequalities. The application of social justice and equity, diversity, inclusion (EDI) frameworks may help expand interdisciplinary perspectives in addressing inequalities. This review study conducted an environmental scan for existing syntheses of theories, models, and frameworks (TMFs) relevant to the social justice and EDI. Results from Web of Science, Scopus, PubMed, CINAHL, PsychINFO, and MEDLINE retrieved an existing implementation science framework intently centered upon health inequalities, and draws from a synthesis of postcolonial theory, reflexivity, intersectionality, structural violence, and governance theory. Given this high degree of relevance to the objective of this review, the framework was selected as a basis for expanded synthesis. Subsequent processes sought to identify social justice TMFs which could be integrated into the base framework selected, as well as to refine scope of the study. Based upon considerations of level of evidence and non-tokenistic integration, the following social justice and EDI TMFs were identified: John Rawls' theory of justice; Amartya Sen's Capabilities Approach; Iris Marion Young's theories of justice; Paulo Freire's critical consciousness; and critical race theory (CRT). The focus of the synthesis performed was scoped towards minimizing potential harms arising from actions intending to reduce inequalities. EDI considerations were not collated into a singular construct, but rather extended as a separate component assessing inequitable distribution of risks and benefits given population heterogeneity. Reflexive analysis amended the framework with two key decisions: first, the integration of environmental justice into a single construct, which helps to inform Rawls' and Sen's TMFs; second, a temporal element of sequential-analysis was employed over a unified output. The result of synthesis consists of a three-component framework which: (1) presents sixteen constructs drawn from selected TMFs, to consider various harms or potential reinforcement of existing inequalities; (2) aims to de-invisibilize marginalized groups who are noted to experience inequitable outcomes, and acknowledges the presence of individuals belonging to multiple groups; and (3) synthesizes seven considerations related to equitable dissemination and evaluation as drawn from TMFs, separated for sequential analysis after assessment of harms.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".