FEASIBILITY STUDYSTUDY: INTRODUCING “ONE-IN-ONE-OUT” IN THE\nEUROPEAN COMMISSION Final Report for the German Ministry for Economic Affairs and Energy Presented by the Centre for European Policy Studies, 5 December 2019
Bibliographic record
Abstract
The need to consolidate and streamline the stock of legislation and reduce the\nunnecessary costs associated with legal rules has been increasingly felt by regulated\nstakeholders and governments in many developed and emerging economies. In\nmany OECD countries, including many EU Member States and Canada, Korea, Mexico,\nthe United States, this has led governments of various political orientations to introduce\nforms of regulatory budgeting, in which administrations are asked to identify, whenever\nnew provisions introduce regulatory costs, existing provisions that could be repealed or\nrevised, thereby offsetting the cost increase. In some countries these rules have implied a\none-to-one offset, whereas in other countries the provisions imposed also a reduction, as in\nthe case of UK’s one-in-two-out and one-in-three-out rules, and the US one-in-two-out rule.\nThis is why we generically refer to these rules as “One-In-X-Out”, or OIXO. Moreover, many\ncountries have also experimented with a complementary strategy, which implies the setting\nof ad hoc burden reduction targets, either for all legislation or for specific sectors. In fact,\nOIXO-rules are just a specific from of burden reduction targets – with the level of the target\n“Out” being linked to the flow of new regulations “In”.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".