Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This book provides a thematically organized examination of political party leadership selection. There is widespread agreement that political party leaders are increasingly important in election campaigns, governing and internal party decision making. Making use of a unique data set that includes more than 1000 leadership elections from over 100 parties in 14 countries over an almost 50 year period, this book provides a comparative examination of how parties choose their leaders and the impact of the different decisions they make in this regard. Among the issues examined are how leaders are chosen, the factors that result in parties changing their selection rules, how the rules affect the competitiveness of leadership elections, the types of leaders chosen, the impact of leadership transition on electoral outcomes, the factors affecting the length of leadership tenures and how leadership tenures come to an end. This volume is situated in the literature on intra-party decision making and party organizational reform and makes unique and important contributions to our understanding of these areas. The analysis includes parties in Australia, Austria, Belgium, Canada, Denmark, Germany, Hungary, Israel, Italy, Portugal, Romania, Spain, Norway and the United Kingdom.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it