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.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".