Bibliographic record
Abstract
Exploring the development of algorithms in Lacanian theory, specifically the "R schema" in the 1950s, I argue that psychoanalysis, read through contemporary debates about the "algorithmic cult" of Netflix and other avatars of popular culture, can be said to reveal the inhuman, machinic essence of subjectivity. The etiology of algorithms, mathemes, and other formulae and diagrams in Lacan’s oeuvre has been under-studied, in part because for some readers they are not as attractive as his more bravura flourishes of word play as exegetical excess, and in part because they derive largely from the ‘hard’ structuralist moment of his work in the 1950s, largely eclipsed in Lacan studies by interests in the ‘Late Lacan’ period of the Sinthome, the knots, jouissance and the semblant. Here I extend (and refine) arguments I began in Does the Internet Have An Unconscious, determining that algorithms in Lacanian theory help us understand the split subjectivity of internet discourse.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".