Bibliographic record
Abstract
Samarin (1967: 1), a classic text about linguistic fieldwork in the 20th century, defines fieldwork as ‘primarily a way of obtaining linguistic data and studying linguistic phenomena’. From the perspective represented in Samarin’s text, fieldwork is conducted by linguists for scholarly and academic purposes, involves cooperation between linguist and language speaker(s) (or ‘informant(s)’), and can be characterized as linguist- centred (Rice 2006; Czaykowska-Higgins 2009) in the sense that it involves research on language controlled by the agenda of the linguist. In this paper, we place the practice of fieldwork involving North American languages within the history of colonization, the terrain of Indigenous communities, and the activist landscape of language revitalization and reclamation. From our different positionalities, as academics, as educator and linguist, as Lil’watul and settler-Canadian individuals, we survey ways in which language fieldwork has changed in North America since 1967, including in relation to collaborative community-based practice, community control, broadening the scope of language work, and re-defining expertise. Community-centred language fieldwork provides for mutuality and benefit in documentation, community goals, and academic interests.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".