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Record W2238086212 · doi:10.15307/fcj.25.185.2015

FCJ-185 An Algorithmic Agartha: Post-App Approaches to Synarchic Regulation

2015· article· en· W2238086212 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Fibreculture Journal · 2015
Typearticle
Languageen
FieldComputer Science
TopicReinforcement Learning in Robotics
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceWorld Wide WebInternet privacy

Abstract

fetched live from OpenAlex

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'.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.259
Teacher spread0.124 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it