Lebanese teacher unions in turbulent times: Their challenges and resistance
Bibliographic record
Abstract
Teacher unions worldwide are being criticized for disregarding their responsibility as professionals towards education and students and for focusing solely on improving teachers’ working conditions. This criticism emerged because of neoliberalism that attempted to dismantle the power of unions. This criticism also does not consider the challenges teacher unions are facing. Hence, this study utilizes a qualitative research design and specifically a grounded theory approach that examines these challenges in the Lebanese context from the perspective of unions leaders and union members (active and inactive members). In-depth individual interviews, formal field interviews, and focus group interviews were used for data collection on 17 public school union leaders and 21 teachers (12 active union members and 9 inactive union members). Findings of this study revealed that there are organizational, legal, political, teacher-related, social, and economic barriers that prevent teacher unions in Lebanon from fully assuming their role. Teacher unions face organizational barriers such as loyalty of union members and leaders to political sectarian leaders that affect union decisions. For political barrier, the ineffective policymaking processes in the Ministry of Education and Higher Education has shown a clear neglect of public schools that severe and deeply rooted problems in these schools.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.010 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".