Aboriginal conditions : research as a foundation for public policy
Bibliographic record
Abstract
Tables and Figures Acknowledgments Introduction: The Focus of Aboriginal Conditions / Jerry P. White Part 1: Thinking Outside the Box: Building Models Based on Communities / Jerry P. White 1. Social Capital, Social Cohesion, and Population Outcomes in Canada's First Nations Communities / Jerry P. White and Paul S. Maxim Part 2: The Limits of Our Knowledge and the Need to Refine Understandings / Jerry P. White 2. Perils and Pitfalls of Aboriginal Demography: Lessons Learned from the RCAP Projections / Don Kerr, Eric Guimond, and Mary Jane Norris 3. Impacts of the 1985 Amendments to the Indian Act on First Nations Populations / Stewart Clatworthy 4. Changing Ethnicity: The Concept of Ethnic Drifters / Eric Guimond 5 . Aboriginal Mobility and Migration Patterns and the Policy Implications / Mary Jane Norris, Marty Cooke, and Stewart Clatworthy Part 3: Confronting Culture with Science: Language and Public Policy / Jerry P. White 6 . Aboriginal Language Retention and Socio-Economic Development: Theory and Practice / Erin O'Sullivan 7. Aboriginal Language Transmission and Maintenance in Families: Results of an Intergenerational and Gender-Based Analysis for Canada, 1996 / Mary Jane Norris and Karen MacCon Part 4: Measuring and Predicting Capacity and Development / Jerry P. White 8. An Application of the United Nations Human Development Index to Registered Indians in Canada, 1996 / Daniel Beavon and Martin Cooke 9. Dispersion and Polarization of Income among Aboriginal and Non-Aboriginal Canadians / Paul S. Maxim, Jerry P. White, and Dan Beavon 10. Toward an Index of Community Capacity: Predicting Community Potential for Successful Program Transfer / Paul S. Maxim and Jerry P. White Conclusion: The Research-Policy Nexus -- What Have We Learned? / Jerry P. White Notes on Contributors Index
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
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".