Centring anti‐oppressive justice: Re‐envisioning dentistry's social contract
Bibliographic record
Abstract
OBJECTIVES: We re-envision dentistry's social contract and elaborate on the idea that it is not neutral and free from such things as racism and white supremacy and can act as a tool of oppression. METHODS: We critique social contract theory through examination of classical and contemporary contract theorists. More specifically, our analysis draws from the work of Charles W. Mills, a philosopher of race and liberalism, as well as the theoretical and praxis framework of intersectionality. RESULTS: Social contract theory supports hierarchies and inequities that may be used to sustain unfair and unjust differences in oral health between social groups. When dentistry's social contract becomes a tool of oppression, its practice does not promote health equity but reinforces damaging social norms. CONCLUSION: Dentistry must embrace an anti-oppression framing of equity and elevate the principle of justice to one of liberation and not just fairness. In doing so, the profession can better understand itself, act more equitably and empower practitioners to advocate for justice in health and healthcare in its fullest sense. Anti-oppressive justice supports health not as merely an obligation but as a human duty.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".