Anxiety and phantasy in the field: The position of the unconscious in ethnographic research
Bibliographic record
Abstract
This article contributes to the geographical literature on reflexivity by asking what it means to take the researcher’s unconscious seriously in ethnographic research, and proposes psychoanalysis as a theoretical and methodological resource for researching the unconscious dimensions of fieldwork. I begin by describing three moments from my fieldwork with panhandlers and drug users that evince the operation of the unconscious. I then review psychoanalytic work in the social sciences where the researcher becomes the object of analysis and situate the debate on psychoanalytic methodology as an extension of earlier work on reflexivity by feminist geographers. I outline three methods for investigating the unconscious dimensions of fieldwork: analysis, supervision, and case consultation. Summarizing my experiments with these methods, I discuss: the discovery that key elements of my research were inextricably connected to my own anxieties as a researcher, how analysis of a dream from early in the fieldwork revealed phantasies rooted in childhood and a profoundly ambivalent relationship to my informants, and I propose a dialectical method for incorporating the revelations of psychoanalytic reflexivity into research. I conclude by discussing some of the possibilities and consequences of taking the unconscious dimensions of fieldwork seriously.
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.143 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".