Fitting demand and supply: How identification brings appeals and motived together
Bibliographic record
Abstract
In this article participants in two demonstrations are compared.The demonstrations took place at two different squares in Amsterdam, at the same day opposing the same governmental policy.Everything was the same except the organizers and their appeals: labor unions with an appeal in terms of threatened interests, on the one hand, and an anti-neoliberalism alliance with an appeal in terms of violated principles on the other.We hypothesized that social cleavages shape mobilising structures and mobilisation potentials.Thereby this study takes an important yet rarely tested assumption in social movement literature serious; namely that grievances are socially constructed.If indeed grievances are socially constructed, one would expect that organizers rooted in different cleavages issue different appeals that resonate with different motives.What made individuals who were protesting the same governmental policy participate at the one square rather than the other?Organizational embeddedness, identification, and appeals that resonate with people's grievances provide the answer to that question.To test our hypotheses we conducted surveys at both demonstrations.Surveyquestionnaires were randomly distributed (response: anti-neoliberalism 209/42%, union 233/47%).The findings supported our assumptions regarding the influence of the diverging mobilizing contexts on the dynamics of protest participation and revealed a crucial role of identity processes.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".