Bibliographic record
Abstract
The original impetus for this project came from a very particular context of concern.My research in the Caribbean alerted me to the extent to which notions of "purity," "blood," and lately even dna analysis came to figure prominently not just as ways of ascribing Indigenous identities, but also as means of claiming them in light of widespread, categorical assertions by colonial rulers and scholars that these peoples had vanished.To my surprise, similar politics of identity were being instituted in north america -indeed, the interest in dna studies had spread from the U.s. to the Caribbean, and in north america as well I found a concern with blood, purity, and the stigma faced by "black Indians" who were being rejected as claimants to Cherokee citizenship.In Canada, at least some band councils, such as that of the saint Regis Mohawk Reserve, have blood quantum requirements of not less than 25 per cent and issue their own tribal Id cards.also in Canada, one can repeatedly hear or read some Euro-Canadians referring to this or that public figure as "not a real Indian … he looks white," the kind of statement that references phenotype, is framed by stereotypes, and applies "mixture" as if it were a diluting factor which one can just as easily encounter in australia or the Caribbean.If race, blood, and even dna were so prevalent, could we find similar concerns spread out across all of the americas?If so, why?If not, why not? are race, blood, and dna essentially the same thing?These were the very first, seemingly very simple questions that led to the emergence of this project.This book has now been years in the making and has gone through many different stages of development and transformation, each time seemingly with a different combination of participants.since its inception in 2006 as a session comprising two panels held at Concordia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.555 | 0.377 |
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".