FCJ-185 An Algorithmic Agartha: Post-App Approaches to Synarchic Regulation
Bibliographic record
Abstract
This rather suggestive and altogether speculative essay began as an attempt on our part to use a model of bio-chemical signal-transduction (Howard Rasmussen's schema for 'synarchic regulation') to explain, beyond the boundaries of cell-transduction in molecular chemistry, transduction in cell-phone applications: the 'synarchic regulation' -and rather remarkable reticulation -of 'cellular transmission' in the techno-communicational rather than bio-chemical field.It was to be a complement and/or an alternate perspective to our conference-paper and subsequent book-chapter on the 'app-alliance' both of which had been written in and for the event of the Apps and Affect conference in October 2013.It became something slightly different, unmoored from mere cellular transmission as such and suggestive of a much more general and more comprehensive techno-scientific, marketeconomic and politico-military -or 'synarchic' -network, operating as the regulative engine for an emerging and overarching planetary system of algorithmic governance.In what follows, we offer an 'app'lication of the principles of 'synarchic regulation' to the field of 'algorithmic governance'.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".