Decentering the Subject, Psychoanalytically: Researching Imaginary Spacings through Image-Based Interviews
Bibliographic record
Abstract
Since the more-than-human turn, geographers have increasingly called for a decentering of the human subject by breaking away from a classically modern understanding of subjectivity and by treating humans as one of many players. In this article, we offer an alternative way of decentering the subject by following the psychoanalyst Jacques Lacan. Far from being subject-centered, psychoanalysis aims to understand the subject as a radically decentered and fragile production, which is only secured through what Lacan calls the imaginary. The imaginary combines two realms—image and imagination—and focuses on how the subject generates a sense of the self through spatial identification with images. Based on image-based interviews conducted in Singapore, Vancouver, and Berlin following the method of photo-elicitation, we demonstrate how this imaginary subject can be empirically investigated. We identify five stages in the interviews that help us retrace how the subject establishes an imaginary relationship with an image as well as how it is confronted with the fragile constitution of this relationship. We conclude by emphasizing the potential of image-based interviews to investigate the decentering of subjects and explore ways in which geographers can further decenter the subject psychoanalytically.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| 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".