A Case Study in Personal Identification and Social Determinants of Health: Unregistered Births among Indigenous People in Northern Ontario
Bibliographic record
Abstract
Under international law, birth registration is considered a human right because it determines access to important legal protections as well as essential services and social supports across the lifespan. Difficulties related to birth registration and the acquisition of personal identification (PID) are largely regarded as problems specific to low-income countries. For Indigenous people in northern and rural Canada, however, lack of PID, like birth certificates, is a common problem that is rooted in the geography of the region as well as historical and contemporary settler colonial policies. This communication elucidates the complicated terrain of unregistered births for those people living in northern Ontario in order to generate discussion about how the social determinants of health for Indigenous people in Canada are affected by PID. Drawing on intake surveys, qualitative interviews and participant observation field notes, we use the case study of "Susan" as an entry point to share insights into the "intergenerational problem" of unregistered births in the region. Susan's case speaks to how unregistered births and lack of PID disproportionately impacts the health and well-being of Indigenous people and communities in northern Ontario. The implications and the need for further research on this problem in Canada are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".