MétaCan
Menu
Back to cohort
Record W1934157515 · doi:10.38140/jjs.v33i1.2951

Gronde vir die weiering van toegang tot inligting soos van toepassing op openbare instellings (deel II)

2008· article· af· W1934157515 on OpenAlexaboutno aff
Benita Roberts

Bibliographic record

VenueJournal for Juridical Science · 2008
Typearticle
Languageaf
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsTheologyPhilosophy

Abstract

fetched live from OpenAlex

The right of access to information as contained in section 32 of the Constitution of the Republic of South Africa, is not an absolute right. It has to be limited in order to protect sensitive material of which the disclosure may cause damage to individual or public interests. In light of this, the Promotion of Access to Information Act 2 of 2000 (PAIA) contains a number of legitimate grounds for the refusal of information. Part I of this article provided an explanation of the structure of the grounds for refusal, looked at the principles relevant to their correct interpretation and continued with an analysis of the first six grounds as contained in the PAIA. In part II, the remainder of the grounds for refusal that are applicable to public institutions, are examined (sections 40 to 46). These grounds, amongst others, relate to records concerning the defence, security, international relations and economic and financial welfare of the Republic, records that reveal research information of a third party or a public institution and records concerning the operations of public institutions. As was the case in part I, the grounds are analysed with reference to corresponding provisions of the American Freedom of Information Act, the Canadian Access to Information Act, the New Zealand Official Information Act and the Australian Freedom of 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0220.005
Scholarly communication0.0020.004
Open science0.0040.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.354
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal for Juridical ScienceSame topicLegal Issues in South AfricaFrench-language works237,207