Political and Economic Factors Influencing Strike Activity during the Recent Economic Crisis: A Study of the Spanish Case between 2002 and 2013
Bibliographic record
Abstract
The Great Recession and the upsurge of widespread social movements in various crisis-ridden countries have given new impetus to the debate on the relationship between economic breakdown and the occurrence of collective action. I revisit the issue by examining strike activity in Spain between 2002 and 2013. For a better understanding of the continuities and changes, I contrast two sets of literature on industrial conflict. The first deals with economic factors influencing strikes or, in other words, with the question of whether and how fluctuations in manpower supply and demand account for continuities and changes in strike activity. The second advocates for a look beyond the economy, towards the political exchange that takes place between unions and state actors and which, depending on its positive or negative nature, leads to shifts of the distributional struggle away from the marketplace towards the public arena or vice versa. The findings reveal that, rather than exclusive, the two perspectives prove to be mutually conducive and are most significant when they are combined. The political exchange model is helpful for understanding the rather stable or even declining strike frequency prior to the economic crisis but also the three nationwide general strikes in 2010 and 2012, which represented a rupture in the social consensus. If the general strikes are left aside, the economic variables come into play: an increased strike frequency during the economic crisis is in fact accompanied by a shift towards smaller strikes related to a single workplace, and to so-called “defensive” strikes. This indicates that an actual decrease in workers’ bargaining power was overcompensated by a growing number of circumstances in which the recourse to strike action became a means of last resort.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".