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Record W1993336473 · doi:10.3109/16066359.2014.942295

Addiction and the adaptive cycle: A new focus

2014· article· en· W1993336473 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

VenueAddiction Research & Theory · 2014
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsLakehead University
Fundersnot available
KeywordsAddictionPsychologyFunction (biology)PhenomenonStability (learning theory)Cognitive psychologyPsychological interventionComputer scienceControl theory (sociology)NeuroscienceArtificial intelligenceBiologyPsychiatryMachine learningControl (management)

Abstract

fetched live from OpenAlex

This paper explores addiction through the lens of complex adaptive systems theory, as an emergent, non-linear phenomenon that undergoes cyclical patterns of stability and change. Particularly, an addiction is a behavioural pattern that emerges through the dynamic interactions of numerous variables operating both within the individual and in the environment. Furthermore, we argue that an addiction moves through the four phases of the adaptive cycle and exists at a given scale nested within a panarchy of other complex systems. Each of these complex adaptive systems is moving through its own adaptive cycle at faster and slower rates, affecting the course of addiction in various ways. We conclude this work by suggesting that forthcoming addiction interventions and research may benefit from the consideration that addiction is a function of three separate, but related, adaptive cycles; the addiction cycle itself; a transitory cycle, and a final cycle in which the individual is actively responsible for the maintenance of his or her own recovery.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.078
GPT teacher head0.435
Teacher spread0.357 · 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