Kindred Practice: Experiences of a Research Group Working Towards Decolonization and Indigenization in the Everyday
Bibliographic record
Abstract
This paper engages the question of kindred practice as contemplated by three members of a research group working toward decolonization and Indigenization. The focus on kindred practice is informed by the title and description of the wâhkohtowin: Indigenizing practice, linking kindred spirits conference held in September 2014, at the University of Saskatchewan. Being each positioned uniquely in relation to social power hierarchies, for the authors, the notion of what it means to be kindred in advocacy carries nuanced dynamics and inflections. As the piece unfolds, each author elaborates key attributes of practice believed to both underlie our kindred relationship and align with the ends of decolonization and Indigenization oma masinahikan ota e-ki-nistokamâtocik e-nanitonâhkik tânisi cihkâhtaw peyakwan e-ki-isi-wâpahtamihk oma kâkwe-kweskipimâtisihk ekwa ka-kiwe-totamihk iyiniwewin. e-ki-mâmiskohtamihk esa wâhkohtowin: iyiniwewin, kâki-mâmawi-api nocihtowipisim 2014, kihci-kiskinwamâtohwikamikohk U of S. oma mina kitapicik ekwa ka-isi-atoskâtahkik, wâpahtamok mitoni cihkâhtaw peykwan e-isi-nayâhtakik oma kâkwe-kweskipitâhkik kihci-kiskinwamâtohwikamikohk ekwa ka-mâmawi-kamâtocik.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.046 | 0.064 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".