A Genre of Failed Novelists and Poets: Exploring Ethnography through a Narrative Lens
Bibliographic record
Abstract
Summary Guided by Ruth Behar’s provocation to explore how ethnography was born out of the writings of novelists and poets and building on a special issue of Anthropology and Humanism on the art of ethnography published in 2007, I explore the histories, potentials, and boundaries of ethnography as a genre and craft. Relying on narrative theory as a resource that can enrich ethnography, I provide a close reading of several ethnographies, focusing on issues of character, time, and plot. I argue that a focus on narrative helps ethnographers put in conversation multiple selves’ shifting roles in ethnography. Narrative provides tools to put in dynamic dialogue these different selves, animate our texts, and write more accessible and enjoyable ethnographies. On another level, consulting with narrative theory is a reminder to claim all our ancestors and take pride in ethnography as a queer genre whose strength lies in its openings and porous boundaries.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".