Collecting Sensorial Litter: Ethnographic Reflexive Grappling With Corporeal Complexity
Bibliographic record
Abstract
In this three-part narrative paper, I put forward “collecting sensorial litter” as an innovative method for helping ethnographers reflexively grapple with complicated corporeality during fieldwork. First, I highlight the continued need for experimentation with body-based reflexive methods that can help capture the messiness of ethnographers’ experiences, especially for sensuous, embodied forms of ethnography. Second, I use theories of intensity and embodiment to conceptualize the “too intense experiences” that are refuse/d by ethnographers’ bodies (e.g., fleeting, whirling emotions; spatial disorientations). Third, I draw upon my fieldwork to illustrate that such experiences are not lost when refuse/d, but manifest symbolically and materially as “sensorial litter.” I detail my methodological process for: A) identifying B) re-claiming and C) reflexively considering three pieces of sensorial litter. I argue the value of collecting sensorial litter includes enhancing self-communication, attending to uncomfortable power relations, and rendering visible critical data (perhaps) inadvertently thrown away in research.
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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 | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".