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Record W2395014866

A (Cybernetic) Musing: Control, Variety and Addiction.

2004· article· en· W2395014866 on OpenAlexaboutno aff
Ranulph Glanville

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsCyberneticsVariety (cybernetics)Control (management)AddictionPsychologyComputer scienceNeuroscienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Medicine is a science of control and should, one might imagine, be a subject that is particularly open to cybernetic investigation and enlightenment, for cybernetics, according to the subtitle in Wiener’s (1948) eponymous book, is concerned, at its heart, with control and communication. In contrast, I have reported, on several occasions, on cases that originate in the cybernetic literature and with the grand old men (yes, I’m afraid, men) of cybernetics, who have discussed systems that are in essence uncontrollable. And there is, in my opinion, a medical predicament that is best dealt with not by controlling it but by giving up all attempts at control—which is the new element I introduce in this paper. I will recapitulate the argument about the uncontrollable here (before discussing the medical condition), for it is powerful and interesting, and perhaps not as well known as it should be, giving, as it does a tremendous sense of scale. I have explored it more fully in “A (Cybernetic) Musing: Variety and Creativity ” (Glanville, R 1998), a paper published in Cybernetics and Human Knowing, an associated journal of the ASC, where the officially published version of this paper may also be found. 2 The reason to recapitulate is firstly to help those who are not familiar with the argument (whether made by earlier scholars, or with the twist that I add); secondly, the hope that a restatement, specially a brief one, will bring some greater clarity and terseness that may make the argument more memorable to those who have already read it in more extended form; and thirdly to more accurately reflect the keynote I delivered at the ASC meeting in Toronto, August 2004 (the slides of which are reproduced—without animations—on the ASC web site)!

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.360
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2004
Admission routes1
Has abstractyes

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