On shifting roles and responsibilities in Canadian indigenous Community-Based Language Research
Bibliographic record
Abstract
In the last 20 years, linguists and community members engaged in fieldwork with endangered languages have become increasingly aware of and vocal about the ethical dilemmas that potentially can, and often do, arise in work of this nature. One result of this can be seen in the reconceptualization of best practices and methodologies in linguistic fieldwork. There is a strong push towards collaborative, community-driven, and interdisciplinary forms of research, and a concomitant shift in the roles taken on by academic and community-based researchers. The shifts in roles in turn have led academics and community-members to rethink the responsibilities associated with these roles. The purpose of this thesis is threefold: firstly, to provide a description of a highly collaborative, community-driven project involving, as one of its components, the documentation of language associated with Traditional Ecological Knowledge (TEK); secondly, to consider and illustrate how the relative roles of academics and community participants have shifted and thirdly, to discuss responsibilities associated with the protection of the TEK documented through this work – knowledge that would not have been documented to the same extent if the project had not been collaborative and community-driven. These topics evolved from the knowledge and guidance of Kʷakʷaka'wakʷ Clan Chief Kʷaxsistalla (Adam Dick), as well as the ethnoecological/linguistic projects that this thesis is centred on. I propose that a) collaborative research that is community-steered can be supported by the Community-Based Language Research model developed by Czaykowska-Higgins (2009), b) within this emerging research framework unconventional research roles can be assumed by all participants, c) it is important to respect and protect the Traditional Ecological Knowledge recorded in research with Indigenous experts, and d) existing systems of Intellectual Property fall short in adequately protecting and respecting TEK. I conclude this study by relating these issues to larger movements occurring within linguistics and social science and humanities research in general. I suggest a move away from subscribing to the Intellectual Property system, and towards approaching language research through a human rights framework. The result of this thesis is an analysis of collaborative community-based language research with and within an Indigenous community in Canada. It will contribute to the ongoing discussions and evaluations of changing roles and responsibilities in field research in linguistics.
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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 | 0.119 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.125 | 0.109 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".