(Re)telling a dog story from Newfoundland: Voice, alterity and the art of ethnographic description
Bibliographic record
Abstract
This paper addresses the question of how and why we (anthropologists and sociologists) tell stories of real people doing real stuff. It will consider this question by reflecting on three versions of a story that I have carried with me and told in variety of contexts over a couple of decades. The story is not mine but was originally told to me by a man while I was visiting a village on the coast of Newfoundland, Canada. In (re)telling three versions of this story I will be focusing on the problem of “voice” and how the voice of the other is constituted. In answering the question of how and why we tell tales of the field, I will suggest that we do so in part so other people, other voices, come to inhabit our accounts thereby rendering them “ethnographic.” The paper will conclude by arguing that our finely detailed accounts play a crucial role in both constituting the authoritative voice of the anthropologist and troubling this voice with the ghostly whispers of other voices which inhabit our narratives even if, as is the way with ghosts, they can never be wholly conjured into full presence and complete intelligibility.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".