Bibliographic record
Abstract
For many of us, doing psychoanalytic geography demands something akin to a leap of faith. Questioning this assumption, the main purpose of this paper is to shift the terms of discussion about doing psychoanalytic geography from the realm of faith to critique. Drawing on Joan Copjec’s, Read My Desire: Lacan Against the Historicists (1994), I argue that much of the uncertainty surrounding the research practices of psychoanalytic geography results from inadequate understandings of two fundamental and interrelated psychoanalytic principles. First, causes and effects cannot occupy the same phenomenal terrain. Second, the taking place of society involves a split between appearance, that is, its observable positive facts and relations, and being, that is, its generative principle and the mode of its institution. According to Copjec, a syncopated relation between being and appearance is not only central to Jacques Lacan’s concept of desire; it is also a neglected axiom that distinguishes psychoanalytic from historicist accounts of the spatial and temporal configurations of society. But what is desire and how can we become, to use Copjec’s phrase, ‘literate in desire’? To answer this question, I explore the empirical example of the fictional comic character Alan Partridge (played by Steve Coogan) who exemplifies the taking place of desire as a self-hindering process in terms of the illusoriness, opacity, and duplicity of language.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".