The Emerging Indigenous Language Economy: Labour Market Demand for Indigenous Language Skills in the Upper Great Lakes
Bibliographic record
Abstract
Language revitalization is necessarily intertwined with economic spheres, as Grenoble and Whaley have expressed that the economic wellbeing of a community influences its ability to engage in such efforts (2006, p. 44). Conversely, health researchers assert that cultural continuity, in which language is inextricably linked, is a prerequisite to self-sufficiency and community sustainability (Oster, Grier, Lightning, Mayan, & Troth, 2014). Nonetheless, the place of Indigenous language(s) within labour market research has often focused on the need for greater access to dominant-language education (MacIsaac & Patrinos, 1995) or the impact on wage differentials (Chiswick, Patrinos, & Hurst, 2000) while research on Indigenous language revitalization in Canada has been largely silent on the relationship to economic spheres, and community economic development literature has engaged with notions of culture more broadly. Drawing on interviews and focus groups from a selection of Anishinaabe communities in Northern Ontario, Canada, this research identifies existing needs for Anishinaabe language speakers within the regional labour market, showcasing the oft-overlooked economic demand for Indigenous language skills. Support for this project was provided by the Ontario Human Capital Research and Innovation Fund from the Ministry of Training, Colleges and Universities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".