Finding common ground in the fisheries field: Agency in the ethnographic encounter
Bibliographic record
Abstract
This paper discusses a significant ellipsis in the theory of ethnographic methodology, specifically, the agency of actors collaborating to build an iterative fieldwork process. This crucial process involves the agency of both the researcher and the researched, and creates a common space for cooperation. Especially absent in anthropological theory about the process is the role that our informants play in attracting and holding our attention throughout an ethnographic encounter. The paper examines a case drawn from recent research among commercial inshore fishers in the Scotia-Fundy region of Canada in order to track this iterative process. One tracking mechanism is an analysis of the conversations that take place between researchers and informants in the early stages of the research, with particular attention to the processes of self-representation involved. These processes of representation draw on polarized political meanings with various actors deploying these meanings in different ways. The paper argues that this iterative process can be better theorized and researched and suggests several ways to do this.
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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 | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.026 | 0.079 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".