Power and Public Administration: Applying a Transformative Approach to Freedom of/Access to Information Research
Bibliographic record
Abstract
Freedom of Information (FOI) or Access to Information (ATI) legislation regulates the right to make written requests for government records.Previous research either interrogates the effectiveness of FOI/ATI legislation for advancing government transparency or positions the requests as a means for gathering data on government institutions.This article intervenes in this debate by treating FOI/ATI mechanisms as the vantage point from which to examine questions about power in public administration.Adopting a transformative approach, this article explores the potential of using FOI/ATI requests as a liberatory tool that enables the analysis of power dynamics in public administration.For this purpose, the author draws upon her experience with Canada's ATI regime and requests from the Immigration and Refugee Board.This article documents how ATI requests reveal policy tensions within the Board related to case and performance management, as well as patterns of front-line staff resistance to these measures.It goes on to examine complaints about delays made to the ATI watchdog which expose the attribution of ATI resources towards non-ATI responsibilities.This article highlights the contribution that FOI/ATI requests can make to public administration 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 | 0.077 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.191 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".