FEASIBILITY STUDYSTUDY: INTRODUCING “ONE-IN-ONE-OUT” IN THEEUROPEAN 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 unnecessary costs associated with legal rules has been increasingly felt by regulated stakeholders and governments in many developed and emerging economies. In many OECD countries, including many EU Member States and Canada, Korea, Mexico, the United States, this has led governments of various political orientations to introduce forms of regulatory budgeting, in which administrations are asked to identify, whenever new provisions introduce regulatory costs, existing provisions that could be repealed or revised, thereby offsetting the cost increase. In some countries these rules have implied a one-to-one offset, whereas in other countries the provisions imposed also a reduction, as in the case of UK’s one-in-two-out and one-in-three-out rules, and the US one-in-two-out rule. This is why we generically refer to these rules as “One-In-X-Out”, or OIXO. Moreover, many countries have also experimented with a complementary strategy, which implies the setting of ad hoc burden reduction targets, either for all legislation or for specific sectors. In fact, OIXO-rules are just a specific from of burden reduction targets – with the level of the target “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 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 | 0.100 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".