The Freedom of Information Act as a Methodological Tool: Suing the Government for Data
Bibliographic record
Abstract
The U.S. Freedom of Information Act, enacted in 1966, and the corresponding Access to Information Act in Canada, circa 1983, were designed to make government more open and accountable to the general public. However, neither act has functioned that way, with most requests being made by lawyers, information professionals, corporations, and political parties. Academic researchers, including criminal justice types, have used the act to access a variety of information from government files. For instance, Alan Block (1975; 1980) used old FBI files for his study of Jewish gangsters in New York City. Ward Churchill and Jim Vander Wall (1990a; 1990b) used the act to illustrate government law breaking in the FBI COINTELPRO program, involving Native Americans, the Black Panthers, and other progressive groups. Only rarely, however, have academics elected to take the government to court and file for judicial review of the government's disclosure decisions. This article describes two lawsuits filed by the author, one under each act, and illustrates both the potential of those acts for obtaining data from the government and the pitfalls a potential plaintiff faces when prosecuting the state for a violation of the Freedom of Information Act. The two lawsuits in question are Yeager v. Drug Enforcement Administration (1982) and Yeager v. Canada (Correctional Service) [2003]. This methodological approach undoubtedly falls under more progressive theories of criminology, such as conflict, radical, or critical perspectives, since mainstream researchers rarely resort to this technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".