Bibliographic record
Abstract
This chapter focuses on the pre-accession measures of the European Union (EU) on the question of gender equality in the Czech Republic and Slovakia during the years 1996–2004, with a view also to contributing to our understanding of the manner in which this area can be Europeanized. The main part of the chapter focuses on the quality of gender equality implementation in the two countries before accession in 2004. It is just one of the many projects that the candidate countries from Central Europe had to deal with, and in the process demonstrated clearly the differences and specific conditions of each. Invariably, this had implications not only for the enlargement process but also for the meaning of Europe. As the European Commission put it: Throughout the enlargement process, discussions and negotiations on gender equality have implied more than candidate countries just catching up with EU legislation and process. The creation of an ever closer union of the peoples of Europe and inclusion of these countries within the European Union brings a wealth of experience and achievements from which the existing member states can also learn. This process of mutual amalgamation of what has been achieved across so many countries can be expected to refocus gender equality in Europe and to provide a fresh and promising impetus towards a gender equal society. 1 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".