Bibliographic record
Abstract
The EUROSEM CSR Dataset was created as a part of the Jean Monnet Network EUROSEM: ‘The Politics of the European Semester’ at the University of Victoria’s Department of Political Science (2018-2022). It provides researchers with data on all country-specific recommendations (CSR) put forward by the European Commission in the European Semester. For the dataset, all CSRs to member states of the euro area from 2012 to 2019 have been considered. The data has been manually coded by a group of researchers familiar with language used in official EU publications. Each country has been assigned to two coders who reviewed and coded the CSRs independently. Their codings have been controlled for intercoder reliability. In total, 1875 CSRs are included in the dataset: 517 ‘headline CSRs’ that include the general policy guidance by the European Commission in a broad policy area for the country in question, as well as 1268 ‘sub-CSRs’ which focus on targeted elements of the broader guidance. The EUROSEM CSR Dataset was created with support of the ERASMUS+ Programme of the European Union. The European Commission's support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.064 |
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".