Crossing Many Boundaries in Creating Allies: Personal Encounters to Unfolding Science to Privilege Indigenous Knowledge
Bibliographic record
Abstract
This paper discusses the challenges and experience of two faculty members (one Inuit, one White) as they seek to aid each other in fulfilling the institutional tenure track and program demands made upon them and as they seek to address how to engage teacher candidates in Indigenous knowledge and anti-racist education. There is discussion of practical action and resources for teaching anti-racism through privileging Indigenous knowledge and “unfolding” Eurocentric science, and of the ethical and philosophical challenges and what transpires in negotiating the individual and ethno-cultural difference of each faculty member through an Indigenous gaze (Ermine, 2007). ota masinahikanis masinahâmok tânisi e-ki-isi-âyimihocik oki niso ataskeskesak (peyak ayaskimow, peyak wâpiski-wiyâs) ekwa mina tânisi e-isi-wicihitocik oma kâ-masinahikehecik ekwa mina ohi kiskinwahamâkana tânisi ka-isi-kiskinwahamawâcik iyiniw-kiskihtamowin ekwa namoya ka-pakwâtitohk. mâmiskocikahtew tânisi ka-isi-atoskahtâkik oma namoya ta-pakwâtitohk âpacihtâtwawi kihci-iyiniw-kiskihtamowin ekwa mina ka-taswekinamihk moniyawipinikewin ekwa ta-kwe-miyo-wipinike mâka ka-ahkâm-mâmawi-atoskâtamihk poko soskwâc pakwâweyak ta-iyiniw-wâpahtekowisit.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.043 | 0.044 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".