The Power of Disruptive Protest in Driving Reform: Explaining the Failed case of Labour in Malaysia
Bibliographic record
Abstract
<p>We reflect upon Malaysian labour’s efforts in advocating reform. Its actions to focus political attention on labour issues at the 2013 general election are analysed. Although the election presented a rare opportunity for labour to bring workers’ issues to centre stage, it did not do so. Piven’s theory of “interdependent” power provides a useful lens through which labour’s failure can be analysed. We show the enormous challenges preventing Malaysian labour from activating “interdependent” power. Critically, the state has systematically maintained artificial distinctions to divide working people from each other and fragment them as a class. The use of state force to crush opposition elements and its employment of highly discriminatory industrialisation policies have additionally militated against labour's efforts to mobilise as a class to secure reform. Piven's theory likely has limited applicability in these authoritarian and non-liberal contexts.</p>
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".