FREEDOM OF INFORMATION ACT IN NIGERIA: EXPLORING THE GULF BETWEEN ENACTMENT AND OPERABILITY
Bibliographic record
Abstract
Two years after the enactment of Freedom of Information Act (FOI Act) in Nigeria, scholars have often asked whether the FOI Act marked a departure from the secrecy usually associated with public information and how the passage of the age-long Freedom of Information Bill (FIB) has affected press freedom in Nigeria. Empirical findings have often shown the contrary. It is generally agreeable that the enactment of the Freedom of Information (FOI) Act is a huge victory for press freedom in Nigeria; it is equally a valid contention that the passage of the Act does not guarantee freedom of information in the country. In this study, we review research findings in local and international studies related to the functionality of the FOI, and argue that there is an obvious difference between the enactment and practice of the FOI, particularly in the absence of mechanisms that must be in place to facilitate the workability of the Act and to guarantee the transition from sheer legislation to effective practice of the Act. Keywords: Freedom, Enactment, Operability and Information Act
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".