Openness, inclusion and self-affirmation: Indigenous knowledge in open knowledge projects
Bibliographic record
Abstract
This paper is based on an action research project (Greenwood and Levin, 1998) conducted in 2016-2017 in partnership with the Atikamekw Nehirowisiw Nation and Wikimedia Canada. Built into the educational curriculum of a secondary school on the Manawan reserve, the project led to the launch of a Wikipedia encyclopaedia in the Atikamekw Nehirowisiw language. We discuss the results of the project by examining the challenges and opportunities raised in the collaborative process of creating Wikimedia content in the Atikamekw Nehirowisiw language. What are the conditions of inclusion of Indigenous and traditional knowledge in open projects? What are the cultural and political dimensions of empowerment in this relationship between openness and inclusion? How do the processes of inclusion and negotiation of openness affect Indigenous skills and worlding processes? Drawing from media studies, indigenous studies and science and technology studies, we adopt an ecological perspective (Star, 2010) to analyse the complex relationships and interactions between knowledge practices, ecosystems and infrastructures. The material presented in this paper is the result of the group of participants’ collective reflection digested by one Atikamekw Nehirowisiw and two settlers. Each cowriter then brings his/her own expertise and speaks from what he or she knows and has been trained for.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".