Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.400 |
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 it