Stratarchy, Gender, and Candidate Nomination: Tension Within the Party Organisation?
Bibliographic record
Abstract
Political parties are complex and multifaceted organisations that are comprised of several different ‘faces’ (party on the ground, public office, central office), each having varying competencies, priorities, and influence. This stratarchical nature of contemporary parties can result in internal inconsistencies, especially when decision-making authority over a particular matter is shared between different faces. Drawing on a unique survey of Canadian constituency association presidents, we consider the extent to which the party on the ground prioritises gender when identifying and selecting its local candidate. While the party in the centre often emphasises the need to recruit and nominate more women, the results of our survey indicate that the party on the ground tends to prioritise an entirely different set of candidate qualities. However, local associations that do prioritise candidate gender tend to produce better representational outcomes in the form of more women contesting the nomination. The disconnect between central and local party priorities with regards to the importance of descriptive representation highlights the potential for internal tension and inconsistencies within parties, and provides new insight into why parties often fail to achieve their publicly stated goals when it comes to increasing diversity.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".