Legal And Policy Frameworks To Overcome Public Information Lock Up In Malaysia
Bibliographic record
Abstract
This paper reports a study which aims to develop a legal and policy frameworks to overcome public information lock up in Malaysia. Public information lock up refers to the presence of the laws and policies which impede the citizens’ right to receive public sector information. Previous studies have identified public information lock up arising from colonial-origin legislations and post-colonial legislations which impede citizens’ right to receive information classified as sensitive, prohibited or non-accessible by the Government. The legal impediments have not been fully addressed in Malaysia, either through constitutional protection or sui generis law on the right to information. Hence, the need for an appropriate legal and policy frameworks to be developed. This study compared the laws and policies on citizens’ right to information in the UK, Canada and New Zealand in order to identify the legal and policy measures to overcome public information lock up. A cross-sectional survey using a 5-point Likert scale was also conducted among 40 respondents from government agency, independent statutory body, civil society and academia. The findings of the survey help to provide an insight on the most appropriate legal and policy measures to overcome public information lock up in Malaysia. The legal and policy frameworks are suitable for adoption by legislatures and policy makers and can become a benchmark in pursuing the objective of overcoming public information lock up in Malaysia.
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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".