Bibliographic record
Abstract
The first people I want to recognize are the candidates and voters whom this research is about.Sometimes with quantitative research, it is easy to forget that the figures and plots we make represent their hopes and fears, desires, and intentions.Or, we plot their indifference and carelessness, which have consequences as well.This distance from their experience is especially the case for candidates, who pour months or years of their life into a career that demands an extraordinary amount from them.Political life has its rewards, to be sure, but it has high costs as well.Yet, we need someone to stand for office, and those that do so need to be held to account, but they also deserve our respect and recognition.No book springs up from nowhere, and different people inspired each turn in the path of this book.In more or less temporal order, I should thank Jason Kenney, whose campaign to bring visible minority Canadians into the Conservative party made me realize what an important part of electoral coalitions they are in Canada.Tim Uppal's experience in the 2011 election made it abundantly clear to me that even with the general rules of electoral politicslocal candidates don't matter much and ordinary people pay little attention -a minority candidate could still provoke an extraordinary reaction, both in terms of rejection and loyalty.André Blais' presidential address, and the "prize" of explaining visible-minority support for the Liberal Party made me think this was an academic project worth doing.At the first academic conference I attended, CPSA 2011, I saw Karen Bird present a candidate experiment that found no affinity effects between South Asian and other minority voters, which immediately caught my interest.My reading brought me to Paula McClain and Matt Barreto's work, which was my first serious introduction to Latino and African-American politics research.In a search for explanations, I read Paul Sniderman and discovered social identity theory,
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.466 | 0.303 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".