Building worker power for day laborers in South Korea’s construction industry
Bibliographic record
Abstract
This article examines how unions build worker power for day laborers in South Korea’s construction industry in the context of widespread informality. Drawing upon regional case studies of the Korean Construction Workers Union (KCWU), we find that construction day laborers experience poor working conditions and rampant employment violations under multiple layers of subcontracting that enable capital to bypass existing labor laws and regulations. Despite the regulatory challenges of complex subcontracting systems, unions can still exert direct pressure on firms to improve informal working conditions by securing and enforcing creative collective agreements. Key to this process is the development of regionally-specific forms of worker power that target firms located higher up the subcontracting chain to take responsibility for informal working conditions. Although the scope of influence varies depending on the type of worker power that unions cultivate (e.g. structural, associational, and symbolic), each form of worker power has enabled unions in different regional contexts to establish uniform standards regarding job quality and job security despite formal restrictions on the legal authority of unions as bargaining agents for informal workers. While such approaches require a high level of organizational and strategic capacity, they demonstrate the ongoing relevance of unions in challenging the global turn to informal work through workplace organizing and collective bargaining.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".