Mobile Affects, Open Secrets, and Global Illiquidity: Pockets, Pools, and Plasma
Bibliographic record
Abstract
This article will take up Deleuze and Guattari’s allusive yet insightful writings on ‘the secret’ by considering the secret across three intermingling registers or modulations: as content (secret), as form (secrecy), and as expression (secretion). Setting the secret in relation to evolving modes of technological mediation and sociality as respectively pocket, pooling, and plasma, the article works through a trio of examples in order to understand the contemporary movements of secrets: the memories of secrets evoked in an intimately interactive music video by the band Arcade Fire (as an example of ‘pocket’); the movements of secrecy turned fabulative in the scopic-doublings of airport full-body scanners (as ‘pooling’); and, finally, the collective secretions that come to saturate and stretch around the globe as expressed by liquidity-seeking financial innovations (providing an angle onto ‘plasma’). These three instantiations of contemporary secrecy are framed by a discussion of Julian Assange of WikiLeaks and Mark Zuckerberg of Facebook – truly a couple for our age: each intent, in their own way, upon bringing an end to secrets. Throughout, we try to maintain close attention to the emerging rhythms and dissonances that engage secrecy in a dance between the half-voluntary and the half-enforced.
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 | 0.002 | 0.004 |
| 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 | 0.043 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".