The behavioral determinants of large-scale collective action
Bibliographic record
Abstract
Brief background: This project is part of a PhD research. An analysis in prior chapters of the research of the emergence of climate change policy (internationally and in Canada) in the late 1980s / early 1990s has shown indications that two behavioral factors may have played a role in facilitating the emergence of large-scale collective action on this issue: the availability heuristic and a representativeness/framing effect. The aim of this project is to run a survey experiment with vignette treatments, testing the influence of these factors on individuals' willingness to engage in large-scale collective action. The documentation of this project includes: 1) The main hypotheses of the project; 2) Research design: sample, power analysis, inclusion criteria, translations; 3) Measurement: stated preferences (policy effort) and revealed preferences (a "work for environmental protection" number identification task); 4) Treatments; 5) Attitude measures, control variables; 6) Analysis; 7) The experimental flow and implementation. An anonymized version is attached to facilitate peer review. No data collection (e.g., pilot data) has taken place prior to preregistration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".