COMPULSORY LEARNING ROMANIA IN EUROPEAN CONTEXT
Bibliographic record
Abstract
Although in many respects, the EU imposes a unitary global policy (European Aquis) in compulsory education it gives freedom to the member countries in implementing their policies in national testing of pupils in monitoring schools and education systems. Currently national tests play a very important role in the development of educational policies, their results are analyzed in order to establish measures to reduce disparities between the levels of knowledge or improving vocational training of teachers. The results of these tests are used for many purposes including monitoring of standards, student progress from year to year, providing feedback to students and parents, guidance of teachers' work. However, normally there should be a direct and proportional connection between the results of the assessment and the budget given to each educational institution, the funding being the evaluation result. It should work as a stimulant in obtaining superior results. This link, although it seems to be implied, was difficult to be proved as long as the test results are not made public, nor is it confirmed by the implemented educational policies. Following comparisons made over time between Canada, the U.S. and EU countries it was found that the results of national tests in Europe are not used as a tool for empowerment, involving sanctions or rewards and subsequently to influence the allocation of resources.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".