MétaCan
Menu
Back to cohort
Record W4385871534 · doi:10.59962/9780774854689

Parties, Candidates, and Constituency Campaigns in Canadian Elections

2007· book· en· W4385871534 on OpenAlexaboutno aff
Anthony M. Sayers

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceGeography

Abstract

fetched live from OpenAlex

Political scientists have traditionally examined the democratic process at the macro level. With its unique micro-level focus, Parties, Candidates, and Constituency Campaigns in Canadian Elections provides the first systematic analysis of the local constituency campaigns that are the basis of elections and democracy in Canada. By taking a detailed look at campaigns in seven B.C. ridings during the 1988 “free-trade” election -- the last under the old three-party system -- Anthony Sayers develops a typology of candidates and campaigns. The dynamics of local associations, nominations, and campaigns, including those of former prime minister Kim Campbell and New Democrat Svend Robinson, as well as key strategic events and the role of the media, are reconstructed from interviews with candidates, campaign managers, party strategists, volunteers, and journalists. The 1993 and 1997 elections are then invoked to show that the insights drawn about the nature of constituency politics remain relevant to the new party system. This important contribution to the study of Canadian elections forcefully argues that knowledge of the dynamics at the local level is essential to a full understanding of Canadian polity, its underlying social basis, and the factors that determine successful election campaigns. As such, Parties, Candidates, and Constituency Campaigns in Canadian Elections will intrigue not only political scientists and students of Canadian politics but also election candidates and party strategists.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.353
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations13
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of British Columbia Press eBooksSame topicPolitical Systems and GovernanceFrench-language works237,207