Paniya Voices: A Participatory Poverty and Health Assessment among a marginalized South Indian tribal population
Bibliographic record
Abstract
BACKGROUND: In India, indigenous populations, known as Adivasi or Scheduled Tribes (STs), are among the poorest and most marginalized groups. 'Deprived' ST groups tend to display high levels of resignation and to lack the capacity to aspire; consequently their health perceptions often do not adequately correspond to their real health needs. Moreover, similar to indigenous populations elsewhere, STs often have little opportunity to voice perspectives framed within their own cultural worldviews. We undertook a study to gather policy-relevant data on the views, experiences, and priorities of a marginalized and previously enslaved tribal group in South India, the Paniyas, who have little 'voice' or power over their own situation. METHODS/DESIGN: We implemented a Participatory Poverty and Health Assessment (PPHA). We adopted guiding principles and an ethical code that promote respect for Paniya culture and values. The PPHA, informed by a vulnerability framework, addressed five key themes (health and illness, well-being, institutions, education, gender) using participatory approaches and qualitative methods. We implemented the PPHA in five Paniya colonies (clusters of houses in a small geographical area) in a gram panchayat (lowest level decentralized territorial unit) to generate data that can be quickly disseminated to decision-makers through interactive workshops and public forums. PRELIMINARY FINDINGS: Findings indicated that the Paniyas are caught in multiple 'vulnerability traps', that is, they view their situation as vicious cycles from which it is difficult to break free. CONCLUSION: The PPHA is a potentially useful approach for global health researchers working with marginalized communities to implement research initiatives that will address those communities' health needs in an ethical and culturally appropriate manner.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".