A Guide to Inter-Indigenous Co-Labbing
Bibliographic record
Abstract
Northern Plains inter-Indigenous relations have been affected by racist, gendered, heteronormative colonial laws, policies, fantasies, discourses, and geopolitical borders. American and Canadian settler statecraft apparatuses, which include federal statutes and acts, have worked to codify Indigenous peoples into “monoethnic identities” (Vrooman 2012, 15) as grounds for defining indigeneity, questioning legitimacy, managing populations, severing relationalities, stealing lands and resources, and mitigating Indigenous resistances. While Nêhiyaw scholar Rob Alexander Innes and settler scholar Nicholas P. Vrooman argue that the Iron Alliance—an economic, military and social confederacy comprising Northern Plains Nêhiyaw, Nakoda, Métis, and Anishinaabe multicultural bands (Innes 2021, 94) active from the seventeenth to nineteenth centuries—has been broadly “overlooked by US scholars” (Vrooman 2012, 6), we affirm that Indigenous women and gender-diverse voices have been historically unheard and unseen between the lines of published Iron Alliance historiographies. Our research seeks to activate gendered inter-Indigenous networks, to reconceptualize critical borderland studies, and to situate place (in particular, Winnipeg) as a constellation of kinscapes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".