Bibliographic record
Abstract
Abstract In this paper, I consider how one writes an ethnographic memoir about memories, time, and our fieldwork when our memories, or our interlocutors’ memories, are unreliable, inconsistent, false, or simply missing. Reflecting on a brain injury that resulted during fieldwork, my (dis)ordered memories, and the intense reliance on memory in sociocultural anthropology, I ask what writing would look like for anthropologists if we wrote with the forgetfulness? Imperceptible to most, and escaping clinical and lab evaluations, the e/affects of my brain injury have reshaped how I am in this world and shifted how I approach and understand the ethnographic project. I suggest that by writing with memory loss, by admitting there are gaps and fissures, by embracing the confusion and confabulations, and by acknowledging the paralleled unfinishedness of the ethnographic project, we work toward a reformed anthropology that no longer uncritically esteems memory as the basis for the anthropological project. In doing so, the paper contributes to what Marlovitz and Wolf‐Meyer have called a “psychotic anthropology,” one that disrupts disciplinary ideas about minds, methods, and memoir and contributes to a productively unruly, and inclusive, ethnographic practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| 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".