A digital death drive? Hubris and learning in psychoanalysis and cybernetics
Bibliographic record
Abstract
This paper offers a critique of the fetishisation of 'the digital' in Western culture by bringing together Freudian and Marcusian psychoanalytic theory with Gregory Bateson's cybernetics. In particular, it correlates the cybernetic concepts of analog and digital information with the psychoanalytic conceptual pair of Eros and Thanatos. The psychoanalytic concept of the 'death drive' appears through the cybernetic lens as a fetishistic tendency towards freezing or regressing to lower levels of complexity and sensitivity of learning. With the help of Marcuse and Bateson, I understand the contemporary prevalence of a 'digital death drive' as an inhibition of learning in terms of the nature of the digital and its severing from the analog context. By contrast, by reading Marcuse's concept of Eros as having multiple logical levels (Eros1,2,3) in the cybernetic sense and by comparing these levels with Bateson's multiple logical levels of learning (Learning1,2,3), we come to see Marcusean 'erotic liberation' or 'revolutionary love' not as resulting from simple acts or statements of rebellion against repressive socio-political norms, but rather as being profound, lifelong learning processes, fraught with complexity and difficulty.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.048 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".