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
Because the task of choosing a candidate for a country's highest office is so important, political parties seek to devise more inclusive processes of selection, processes that are commensurate with the party's electoral goals. Often this has involved reforming an existing process in ways that open up the mechanisms of leadership choice to a wider `selectorate'. In such a process parties sometimes undergo changes well beyond what may have been anticipated when the reforms were first introduced. This article examines the process of leadership selection in three political parties that have undertaken major reforms in the process of leadership selection in recent years - the Democratic Party in the United States, the Labour Party in Britain and the Progressive-Conservative Party in Canada. In each instance, it is possible to demonstrate that the method chosen has had significant consequences for the parties themselves and for the party system as a whole, in part because of variations in the inclusiveness of the selectorates created, and also through effects on candidate recruitment.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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