Bibliographic record
Abstract
The purpose of this technical brief is to assess current methodologies for the collection and calculation of teacher costs in European Union (EU) Member States in view of improving data series and indicators related to teacher salaries and teacher costs. To this end, CRELL compares the Eurydice collection on teacher salaries with the similar Organisation for Economic Co-operation and Development (OECD) data collection and calculates teacher costs based on the methodology established by Statistics Canada as explained in Indicator B7 in Education at a Glance (OECD, 2014). This indicator allows for analysing the different factors that influence teacher costs: teacher salaries, teaching time, instruction hours and student/teacher ratios, as well as class size. The analyses will provide specific information on the contribution of the different factors used to derive the Salary Cost of Teachers per Student (CCS) and how they might depend on the way data for the different factors are collected. On the basis of assessing the different forms of data collection with the same methodology, suggestions for development work that could be undertaken to align the Eurydice and OECD data collections are offered.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.018 |
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".