Bibliographic record
Abstract
This concluding chapter provides a quick overview of what the book has accomplished and emphasizes the important points. Colonization sought to destroy Indigenous cultures and, though it was unsuccessful, it did inflict a great deal of hardships on Indigenous Peoples of North America. Since, for Indigenous cultures, relational and community ties are paramount, by breaking these ties over a period of centuries, colonizers undermined the buffering actions of Indigenous institutions against upheavals. The book explains the current predicament of Indigenous Peoples in North America in these terms by building into its model assumptions that rest on facts in plain sight but are usually ignored. In particular, the model seriously attends to the phenomenon of historical trauma and the persistent pain it induces. Scholars have pointed out that European colonization has led to a reversal of fortunes in the colonies; those that, relative to Europe, were previously rich have been reduced to poverty while those that were poor have become rich. This book shows that, for the Indigenous Peoples of North America, the reversal has been even more severe: the theory presented shows that it has resulted in the ongoing phenomenon of ‘Deaths of Despair’. The same theory also offers testable hypotheses on what has made many Indigenous groups exhibit survivance, withstanding centuries of relentless assault. An emphasis on culture is seen to be one of important determinants of 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.017 |
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".