How Neoliberalism Shapes Indigenous Oral Health Inequalities Globally: Examples from Five Countries
Bibliographic record
Abstract
Evidence suggests that countries with neoliberal political and economic philosophical underpinnings have greater health inequalities compared to less neoliberal countries. But few studies examine how neoliberalism specifically impacts health inequalities involving highly vulnerable populations, such as Indigenous groups. Even fewer take this perspective from an oral health viewpoint. From a lens of indigenous groups in five countries (the United States, Canada, Australia, Aotearoa/New Zealand and Norway), this commentary provides critical insights of how neoliberalism, in domains including colonialism, racism, inter-generational trauma and health service provision, shapes oral health inequalities among Indigenous societies at a global level. We posit that all socially marginalised groups are disadvantaged under neoliberalism agendas, but that this is amplified among Indigenous groups because of ongoing legacies of colonialism, institutional racism and intergenerational trauma.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".