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
The "secret garden of politics", where some win and others lose their candidate selection bids, and why some aspirant candidates are successful while others fail have been enduring puzzles within political science. This book solves this puzzle by proposing and applying a universally applicable multistage approach to discover the relationship between selection rules, selectors’ biases, aspirants’ attributes, and selection outcomes. Rare party and survey data on winning and losing candidates and insider views on what it takes to win a selection contest at multiple selection stages are compared and used to reveal the inner workings of the secret garden. With a primary focus on the British Labour party over several elections, the findings challenge many long-held assumptions about why some aspirant candidate types are successful over others and provides real-world and controversial solutions to addressing women’s and other marginalised groups’ descriptive underrepresentation. As such, it provides a much-needed fresh look at party selection processes and draws new conclusions as to why political underrepresentation occurs and should inform policies to remedy it. This text will be of key interest to scholars and students of gender and ethnicity in politics, political parties and candidate selection, and more broadly to the study of political elites, comparative politics, sociology, labour studies, gender, race, and disability studies, and to practitioners.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it