‘Are you a union member?’ Determinants and trends of subjective union membership in Italian society (1972–2013)
Bibliographic record
Abstract
This article analyses so-called ‘subjective union membership’ among employees and non-employees in Italy between 1972 and 2013. Unlike trends drawn from administrative data (‘objective membership’), subjective membership, based on the declaration of the respondent, takes into account respondents’ awareness of being affiliated to a union, their sense of belonging and the social desirability of stating their membership status. Instrumental and ideational rationales inform our cross-sectional and longitudinal hypotheses. Using an ITANES pooled dataset based on 11,073 observations over 40 years (1972–2013), two major findings emerge. First, only a minority of politically engaged left-wing individuals have maintained the same probability of declaring themselves union members since the early 1970s. Secondly, subjective membership has sharply decreased over time not only among employees, but also – in clear contrast to administrative data – among non-employees. Subjective measures are thus particularly useful in improving our understanding of union membership.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".