Bibliographic record
Abstract
This paper examines several fieldwork situations from a community-based language revitalization project taking place in British Columbia, Canada. Through this examination I intend 1) to exemplify possible types of roles played by linguists and community members, with a view to expanding linguists’ perspectives on fieldwork, and 2) to touch upon several interesting implications of changing the roles and relationships of linguists and community-members in fieldwork. There is a growing movement amongst linguists to conduct linguistic research on small Indigenous languages in collaboration with community members (e.g., Yamada 2007, Stebbins 2003). A consequence of conducting research collaboratively is that the roles which outsider linguists and community members take on in fieldwork situations are no longer simply expert/informant types of roles in which a linguist is the outside expert and a speaker is a language-data source (see Rice 2006: 140-145 for discussion of roles). For example, in a Community-Based Language Research model, research on a language is conducted for, with and by the language-speaking community within which the research takes place and which it affects (Author 2008; cf. Grinevald 2003). This model allows for the possibility that community members participating in fieldwork research will be explicitly recognized as experts and as researchers, not simply as informants, consultants, teachers, or even collaborators. As experts, the community researchers direct and lead the research; outsider linguists, in contrast, take on supporting roles. In one fieldwork situation that I discuss, for instance, two elders and their community research assistant defined the focus of their fieldwork and their working methodology. Only once the fieldwork was underway was a linguist asked to provide support in specific aspects of the fieldwork, such as helping to organize a database. One interesting aspect of this fieldwork situation is that the roles that the community members and the linguist have taken on do not fit standard roles assumed by Human Research Ethics Boards and university Research Services. This in turn raises ethical and intellectual questions about ownership and authorship, and practical questions such as whether the elders should sign the usual informed-consent forms to participate in the grant-funded project and how Memoranda of Understanding between the community and university apply to the research. As this example suggests, collaborative research requires linguists to re-define themselves as fieldworkers and researchers, to re-think research roles, and to address new issues. This paper aims to contribute to the redefinition and rethinking.
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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 | 0.052 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.061 | 0.049 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".