Challenges of Teachers’ Remuneration in Latvia, Lithuania and Estonia: The View of Trade Unions as Social Partners
Bibliographic record
Abstract
Uncompetitive remuneration for teachers has been a problem for many decades in Latvia. Government together with social partners –education trade unions, have tried to solve this problem by asking to allocate additional financial resources to public education budget, by developing teacher salary raise schedules and various educational reforms in all three Baltic countries. The aim of the study is to research general principles of teachers’ remuneration and workload in Latvia, Lithuania and Estonia, for comparison and social dialogue argumentation on the part of education trade unions. Research approach is analysis of information on teachers’ salary calculation and workload presented by education trade unions as semi-structured interviews. Reflection on the topic in relation to theoretical sources, including international organizations representing education and social dialogue issues, is enclosed. The results of the research show that there are differences in all three countries regarding general education teachers’ remuneration. The main challenge is the implementation of effective and decision –making oriented social dialogue between trade unions and education policy makers regarding teachers’ weekly contact hours and paid additional hours per full workload, minimal and average monthly salary rate for teachers. The conclusion of the paper indicates that, based on Lithuania and Estonia experience, immediate reforms in Latvia are necessary to increase public funding for education, to increase teachers’ remuneration, harmonize and balance workload and ensure that teachers are paid for all duties performed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".