Bibliographic record
Abstract

 
 
 The purpose of this analysis is to differentiate social movements. In this instance, we will be using the hippie/counterculture movements during the 1960s and 1970s in Canada, and those that are occurring in the second decade of the twenty-first century. In particular, this analysis distinguishes right-wing extremist movements in 2016 from groups like the Hippie Movement and the Black Panther Party Movement. Specific reference will be made to contrast the social movements of the twenty-first century that are non-political in nature but are identity-based, versus movements during the 60s and 70s that were political by design and intent. Due to the non-political nature of twenty-first century Violent Transnational Social Movements, they might be characterized as fifth generation warfare, which we identify as identity-based social movements in violent conflict with other identity based social movements, this violence may be soft or hard. ‘Soft violence damages the fabric of relationships between communities as entrenches or highlights the superiority of one group over another without kinetic impact. Soft violence is harmful activities to others which stops short of physical violence’. (Kelshall, 2019) Hard violence is then recognized as when soft violence tactics result in physical violence. Insurgencies are groups that challenge and/or resist the authority of the state. There are different levels of insurgencies; and on the extreme end, there is the resistance of systemic authority.
 
 
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".