Temporal conflict and challenging the police
Bibliographic record
Abstract
This paper builds on research that shows that differences in the temporal properties of organisations can lead to temporal conflict, posing a barrier to collaboration. It considers how temporal diversity might shape political contention by examining how different temporal properties of New York and Toronto police and protesters manifest in their interactions. Vignettes of three protest events in Toronto and New York City in 2020–21 are constructed from fieldnotes, government planning and police oversight documents and media coverage. These illustrate how police and protesters understand protest events differently and how these collective actors strategically alter the temporal properties of their tactics (pace and duration) and their narratives (temporal orientation and temporal horizon) in order to gain leverage over their opponents. The temporal conflict that ensues varies in intensity, and shapes the sequence, emotional tone and outcome of these events. This shows how differences in temporal properties shape contention; how these properties can be used strategically to gain leverage and suggests that analyses of temporal conflict should be incorporated into research on contentious politics and studies of strategic interaction.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".