Library and Information Sciences in Romanian higher education today
Bibliographic record
Abstract
With 33 years of existence in Romania, higher education in the field of library and information science is today organised by four universities: University of Bucharest, Babes Bolyai University of Cluj Napoca, Lucian Blaga University of Sibiu and West University of Timisoara. In 2005, the Bologna Process was implemented in this field as well, with specialized education being organized in three specific cycles: undergraduate and master's studies, postgraduate studies and doctoral studies. Starting with this moment, we are assisting the attempt to standardise the curriculum, implicitly the set of skills and competences that graduates must have after graduation. This is supervised by the Romanian Agency for Quality Assurance in Higher Education; the competences and skills are listed in the register drawn up by the National Agency for Qualifications, and they are correlated with the ESCO register (European Skills, Competences, Qualifications and Occupations). By analysing the most recent RAQAHE recommendations (updated in 2022), the ESCO and NAQ registers, we aim to highlight the topicality of the study programmes offered by the four universities. The discussions can be held, in essence, on how prepared graduates are for the labour market and what would be the next steps for a more frequent adaptation of the content of subjects in curriculum in order to take into account the views formulated by The European Network Of Public Employment Services in June 2023, as a contribution to the European Year of Skills. It allows us to formulate some conclusions on the current state of curriculum in Romania.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".