Rethinking Freedom of Information Research: Selective Flows of Information in Borders and Migration Studies
Bibliographic record
Abstract
Abstract This paper offers a novel analytical framework to enhance understanding of freedom of information (foi) in borders and migration studies. Drawing on scholarship in science and technology studies which understands scientific activity in terms of ‘knowledge-control regimes’ (Hilgartner 2017), our first section proposes four analytics. These are: a focus on the constitutive power of interactions; a principle of symmetry when it comes to studying what and how things are concealed and how they are made public; a concern with the mediating role of devices and infrastructures; and attention to the cultural as well as legal regimes which shape the conduct and identity of the actors who populate a given field of information control. The second part puts these analytics to work in the study of a particular area of immigration enforcement: the UK’s use of charter flights as a political, logistical and symbolic practice that shapes its deportation operations.
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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 | 0.064 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.131 |
| Scholarly communication | 0.025 | 0.051 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| 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".