Bibliographic record
Abstract
The Candidacy Calculation examines how perceptions of barriers to candidacy in Canada differ by social, economic, and political backgrounds. Through semi-structured interviews with 101 individuals from diverse social backgrounds, geographical locations, and political ideologies, this book uncovers both new and previously overlooked challenges such as online harassment and social media scandals, while also offering a deeper understanding of traditional barriers like financial constraints, work-life balance, employment issues, partisanship, and family responsibilities. The findings demonstrate that individual considerations regarding candidacy are much more complex than previously thought. Drawing on an intersectional approach, the book analyses how factors such as gender, race/ethnicity, sexuality, age, and other social attributes intersect to create unique barriers to political careers, thereby presenting a nuanced view of the candidate emergence process in Canada. By rigorously testing the role of political ambition in fostering diversity in political representation, The Candidacy Calculation compares the experiences of women and men, various social groups, and individuals who have become candidates with those who have not. The book aims to assist policymakers and activists in identifying solutions to overcome barriers and enhance opportunities for increasing candidacy among under-represented groups in politics.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".