Bibliographic record
Abstract
ACCC v Boral Besser Masonry Ltd 388-9 Act against Restraints of Competition, Germany 446 advocacy 14, 185-208 competition culture, building 187-8 direct prohibition of anticompetitive laws, regulations and government actions 198 and enforcement 200 government, influencing 188-91 incremental approach 203-7 independent agency 196 limitations of traditional approach 185, 191-200 implications for competition advocacy 196 institutional arrangements impacting on type/ effectiveness of competition advocacy 196-200 magnitude of political challenge 192-5 political drivers for competition reform 195-6 ministerial system 197 neutral umpire 198 NGOs 151-2 policy approach 200-202 Australia's National Competition Policy 201-2 reconceptualisation of concept 207 reforms making last 206-7 policyholder and stakeholders, reforming 205-6 reform scene 203-5 regulatory impact statement review bodies 198 traditional approach 186-91, 208 limitations of 191-200 Advocacy and Implementation Network (AIN) 106 Advocacy and Implementation Network Support Program (AISUP) 106 Advocacy Working Group, ICN 102 Africa, NGOs in 137 Africa Competition Programme (AFRICOMP) 71-2 African Competition Forum (ACF) 63, 82-4, 138 Agency Effectiveness Workshop 99 Agreement on Trade-Related Aspects of Intellectual Property
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.677 | 0.476 |
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".