Datafication and the practice of intelligence production
Bibliographic record
Abstract
Datafication of social life affects what society regards as knowledge. Jasanoff’s regimes of sight framework provides three ideal-type models of authorised knowing in environmental data practice. This paper applies Jasanoff's framework for analysing intelligence practice through an exploratory empirical study of crime and intelligence practitioners in a selection of police services in Australia, New Zealand, Canada and the United States. The paper argues that the ‘view from somewhere’ (VFS) captures the essence of existing police intelligence practices in the four countries but the ‘view from nowhere’ (VFN) is emerging as a possible future for police intelligence – an approach promoted by technology companies and supported mainly by police leaders and managers. The paper investigates the challenges and limits of a shift by police from VFS to VFN in the production of intelligence; the challenges are primarily political, which threaten the dominance of police contextual knowledge over ‘scientific’ knowledge. These political challenges also have symbolic and material implications. The paper concludes that, because of these challenges, a complete shift from VFS to VFN is not likely to happen. At best the two models might co-exist with the latter subordinate to the imperatives of the former, resulting in further tension between sworn officers and civilians, organisational inertia, as well as technologies that may be under-utilised or abandoned.
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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.011 | 0.105 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".