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Changing Social Focusing in Indigenous Social Movements

2014· article· en· W2299583198 on OpenAlexaffabout
J. David Flynn, James Hay

Bibliographic record

VenueJournal of Sociocybernetics · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsWestern UniversityThe King's University
Fundersnot available
KeywordsCentralityDifferentiationIndigenousSociologyNetwork theorySocial movementSocial changeVariety (cybernetics)Political scienceSocial scienceLawEcology

Abstract

fetched live from OpenAlex

Using complexity science, we develop a theory to explain why some social movements develop through stages of increasing intensity which we define as an increase in social focusing. We name six such stages of focusing: disintegration, revitalization, religious, organisation, militaristic, and self-immolation. Our theory uses two variables from the social sciences: differentiation and centrality, where differentiation refers to the internal structure of a social system and centrality measures the variety of incoming information. The ratio of the two, differentiation/centrality (the d/c ratio) is a shorthand way of saying that centrality must be matched by a corresponding level of differentiation to maintain basic focusing. If centrality exceeds differentiation, then the result is a lack of focusing—disintegration. On the other hand, the more differentiation exceeds centrality, the more the system moves into the higher stages of social focusing, from revitalization to the final stage of self-immolation. To test the theory we examine historically indigenous social movements, in particular, the Grassy Narrows movement in northern Ontario Canada. We also suggest how the theory might be applied to explain other examples of social movement, especially millenarian movements at the end of the 20th century. We also suggest sociocybernetic ways the rest of society and the social movement itself can change its own social focusing.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.101
GPT teacher head0.388
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2014
Admission routes2
Has abstractyes

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