Bibliographic record
Abstract
As every author knows, many personal debts are accumulated, and new friendships made and old ones strengthened, during the long journey of producing a manuscript.I am grateful to the late John Eagle for many discussions of this book in its early stages over several years; and to Rod Wilson, who, over numerous lunches, patiently and engagingly broadened my understanding of Native peoples, and acted as a sounding board for many of my ideas.Merrill Distad, Dale Gibson, Ted Binnema, and Don Smith all read early drafts of the manuscript, pointed out many errors, made many thoughtful suggestions, and always left me encouraged.I acknowledge my debts to them at numerous points in the text and endnotes.I take full responsibility for any errors, omissions, or other shortcomings.I also mostly profited from the comments of anonymous readers, and the book is better for that process.Thanks as well to Lorie Huising, whose technical expertise proved enormously helpful, and to Maurizio Yamanaka, for drawing the maps.I have always learned much from teaching, from having to clarify my ideas for students, and from the stimulation of discussion, debate, and even dealing with student assignments.I miss that in retirement, but want to acknowledge two groups in particular: the fine graduate and senior undergraduate students who studied aspects of Native
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.213 | 0.159 |
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".