Freedom of information and social science research design
Bibliographic record
Abstract
This multidisciplinary volume demonstrates how Freedom of Information (FOI) law and processes can contribute to social science research design across sociology, criminology, political science, anthropology, journalism and education. Comparing the use of FOI in research design across the United Kingdom, the United States, Australia, Canada and South Africa, it provides readers with resources to carry out FOI requests and considers the inuence such requests can have on debates within multiple disciplines. In addition to exploring how scholars can use FOI disclosures in conjunction with interview data, archival data and other datasets, this collection explains how researchers can systematically analyse FOI disclosures. Considering the challenges and dilemmas in using FOI processes in research, it examines the reasons why many scholars continue to rely on more easily accessible data, when much of the real work of governance, the more clandestine but consequential decisions and policy moves made by government ocials, can only be accessed using FOI requests
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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 | 0.211 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".