Modulating Eventfulness: How Liaison Policing Strategies Mitigate Potentiality in Indigenous Land Defence Organising
Bibliographic record
Abstract
Abstract Liaison policing strategies are increasingly deployed as Canada’s frontline response to Indigenous‐settler land disputes. A fusion of pre‐emptive interventions, intelligence gathering, and best practices in public order and community policing, contemporary liaison strategies have important implications for Indigenous self‐determination and decolonial struggles. Yet, despite their rapid proliferation, we know little about how liaison strategies are enrolled as a technique of settler colonial governance. I begin addressing this gap through a four‐year case study tracking how a multi‐agency liaison policing assemblage undermined an urban Indigenous land reclamation in the city of Ottawa, Canada’s national capital. I show how liaison strategies worked by constraining the terrain of manoeuvre for radical organising while simultaneously facilitating Indigenous engagement in state‐sanctioned processes of recognition and accommodation. Arguing that the literature on public order policing inadequately theorises the relationship between liaison policing and settler colonial power, I propose “modulating eventfulness” as a more apposite conceptual grammar.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".