Bibliographic record
Abstract
“Ethical auditing” is regarded as a new device for monitoring the behaviour of firms and governments. the best known example is the EU-guideline for eco-auditing which aims a monitoring compliance with “voluntary” environmental protection standards. Unlike eco-auditing which covers firms, “women's auditing” focuses on national budgets and the behaviour of governments. It is assumed that checking revenue and expenditure item-by-item gives a clearer picture about discrimination than merely looking at tax legislation or sectoral budgets. the first country where women's auditing became institutionalised was South Africa, quickly followed by Australia, Canada, and the labour government in the UK. The paper will give a descriptive analysis of women's budgets. It attempts to clarify in how far item-by-item monitoring does indeed help to overcome the specific asymmetric information problem at stake: Asymmetric information here, takes on the form of governments claiming that budgets are gender-neutral. the high individual search costs for getting access to budget proposals before they pass parliament in combination with the generally low representation of women in parliaments defines a formidable threshold for women. A further question to be addressed is whether or not an institution such as women's auditing can be an effective device in situations in which consumer/voters’ interest are hard to organise.
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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".