Bibliographic record
Abstract
It might be inferred that an economic approach is limited to the analysis of the adverse effects of the erosion of Indigenous cultures due to historical trauma. This is far from the truth. The model offered in this book also speaks to the phenomenon of resilience of Indigenous communities in the face of repeated and ongoing hardships. In fact, a careful investigation of the colonial mechanisms that have wrought devastation to Indigenous Peoples also reveals the factors that have contributed to Indigenous resilience. A term more appropriate than resilience in the case of historical trauma is ‘survivance’, a term coined by Vizenor (2008). The economic model that offers explanations for the adverse effects of historical trauma also suggests what it is that contributes to the flourishing of Indigenous communities. This chapter offers a tentative theory of when an Indigenous community would spiral into a ‘bad’ equilibrium and when it would exhibit survivance. It is seen that the greater is a community’s emphasis on culture and sense of belonging, the more likely it is to exhibit survivance in the face of historical trauma. Communities with low levels of historical trauma are seen to be more likely to exhibit survivance. The analysis brings out the importance of communal, as opposed to individualistic therapies as a remedy. This is seen to be consistent with traditional Indigenous practices that are intended to promote wellbeing and survivance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".