Research Models, Community Engagement, and Linguistic Fieldwork: Reflections on Working within Canadian Indigenous Communities
Bibliographic record
Abstract
This paper reflects on different research models in linguistic fieldwork and on different levels of engagement in and with language-speaking communities, focusing on the Canadian context. I begin by examining a linguist-focused model of research: this is language research conducted by linguists, for linguists; the language-speaking community’s participation is limited mostly to being the source of fluent speakers, and the level of engagement in the community by a linguist is relatively small. I then consider models that involve more engaged and collaborative research, and define the Community-Based Language Research model which allows for the production of knowledge on a language that is constructed for, with, and by community members, and that is therefore not primarily for or by linguists. In CBLR, linguists are actively engaged partners working collaboratively with language communities. Collaborative models of research seem to be closest in spirit to models advocated by Indigenous groups in Canada and elsewhere. I reflect here on (1) why one might choose to work within a collaborative research model, and (2) what some of the challenges are that linguists face when they conduct research collaboratively. In a broad sense the purpose of this paper is to think through some questions that an “outsider” linguist might face when undertaking linguistic research in an Indigenous community today.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.124 | 0.076 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.007 | 0.010 |
| 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".