Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".