Bibliographic record
Abstract
Anishinaabeg (including Odawa, Potawatomi, Ojibwe, Saulteaux, and Chippewa) are striving to maintain and revitalize Anishinaabemowin (their Anishinaabe language) throughout their territories. This research project explored Anishinaabemowin revitalization (AR) efforts through interviews with 22 community activists, scholars, and teachers of Anishinaabemowin as a second language (AnSL). The interviews queried the various methods and mobilization strategies being used and developed. The project provides insights into different AnSL pedagogies and teaching strategies, as well as barriers and challenges facing the AR movement. Interviews were conducted in six Canadian cities as well as four First Nation reserve communities. Research participants discussed innovative AR strategies and techniques while acknowledging concerns that AR has yet to produce enough fluent speakers to reverse the decline of Anishinaabemowin.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".