Power resources, institutional legacy and labour standards transformation: Lessons from two developing countries
Bibliographic record
Abstract
This article assesses the political dynamics behind labour standards reform attempts and their divergent outcomes in Lebanon and Tunisia during the neoliberal era according to a success/failure and flexibility/security configuration. It emphasises the interconnection of three power resources. Institutional legacies mattered for organised labour’s influence on these reforms. Having already had access to formal channels of decision-making since the 1970s, Tunisian labour effectively used these channels during the 1990s reforms. Such channels were not historically well-developed for Lebanese labour and were critical in the reform failure. Furthermore, thanks to its ideational power (i.e. legitimacy), Tunisian organised labour was part of the political coalition of reform. Consequently, Tunisian labour was successful in gaining some job security privileges in exchange for flexibility. Conversely, Lebanese organised labour lacked such legitimacy, which contributed to their exclusion from the political coalition. Instead, employers used their informal channels (e.g. networks) with the political coalition’s elites to halt job security reform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".