When Movements Change Policies: Popular Legislative Initiatives in Favor of Housing Rights in Spain
Bibliographic record
Abstract
ABSTRACT The Spanish housing rights movement has been consistently demanding new regulations of the housing sector. In addition to protests, it has campaigned for popular legislative initiatives (PLI) that have generated different outcomes. How can we account for such variation? To address this question, this article compares three PLI campaigns for housing rights: the national PLI (2011–2013), the Catalan PLI (2014–2015), and the Madrid PLI (2017). Whereas the three PLIs collected enough signatures to be submitted to congress, only two managed to pass the first stage of the legislative process, and only one was eventually turned into law. This study argues that these legislative outcomes must be unpacked and disaggregated into concatenations of stages that were shaped by three processes: coalition building, salience building, and legitimacy building. It uses a process tracing method to show empirically how these dynamics unfolded in the three abovementioned PLIs.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".