Live archives: Freedom of information requests as political methodology
Bibliographic record
Abstract
Abstract Freedom of information requests are an important research tool yet receive comparably little methodological scrutiny relative to other methods commonly used by geographers. This article considers two methodological aspects to freedom of information requests. The first is how they operate as “live archives” that take shape as batches of files are compiled in ways that reflect search terms, negotiations over the scope of requests, bureaucratic processes, and considered judgments of researchers in response to variables both within and beyond their control. The second considers how freedom of information requests operate as a political methodology through the encounter they produce with state bureaucracies. Using examples that cut across these concerns and illuminate some of the ways that methodological scrutiny matters, the article discusses how freedom of information requests present overlapping yet distinct concerns for qualitative research on issues of reflexivity, ethics, and positionality. The methodological concerns that arise are not frequently discussed but, as with other methods, are important to understanding the limits and reach of data collection, analysis, and accessibility both for researchers and for the communities who may have interest in, or be impacted by, geographic research .
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 | 0.151 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.015 | 0.090 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".