Transversality, or How Not to Reproduce the Organisations You Fight
Bibliographic record
Abstract
One of Guattari's most important conceptual contributions is his notion of transversality. This complex notion responds to the very concrete problem that, when we oppose certain power formations, we tend to reproduce these same power formations. This article proposes an examination of Guattari's concept of transversality through five of its aspects, which will be related to the various problematics they respond to. The aim is to show how power formations operate and how transversality responds to them, and also to highlight the primordial importance of practical and political issues to Guattari's conceptual work. As we will see, Guattari conceived of transversality in relation to the Leninist cut; it thus is linked to democratic centralism and to the subject-group. Transversality is also linked to enunciation and autopoiesis; it is characterised by Guattari as transmonadic and transitive. The notion of transversality will also be put in relation to the war machine, on which it will thus shed light.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.074 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".