“Going Local": The Local Dimension of Party Membership Recruitment Strategies in Belgium
Bibliographic record
Abstract
While the decline of national party membership levels in Europe is a well-documented phenomenon (Van Biezen et al. 2012), little is known on how parties organize at a subnational level to recruit members. Much of the literature on party membership has focused on political parties conceived as rational unitary actors (Scarrow, 1994). Yet, political parties are evolving in increasingly multi-layered or multi-level systems (Deschouwer, 2003) with interactions between the different electoral arenas (Hooghe and Marks, 2003; Fabre, 2010). Recent studies in Canada have shown the importance of local party branches organizations to mobilize members and integrate with the party at national level (Pruysers, 2018; Carty and Eagles, 2005). However, there is still little empirical evidence on the impact of local party recruitment strategies on membership level and characteristics. Relying on the 2018 Belgian Local Chair Survey, this paper suggests to investigate the link between local branch affiliation procedures, local contextual variables and membership levels and type.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".