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Record W4388528861 · doi:10.1177/19401612231209163

Book Review: <i>Rejoinder to the Review of Inside the Local Campaign: Constituency Elections in Canada</i> MarlandAlexGiassonThierry (eds.) Inside the Local Campaign: Constituency Elections in Canada. Vancouver, BC: UBC Press, 2022. 448 pp. ISBN: 9780774868198. Available Open Access via UBC Press at https://www.ubcpress.ca/media/9780774868204_web_OA.pdf.

2023· article· en· W4388528861 on OpenAlexfundaboutno aff

Bibliographic record

VenueThe International Journal of Press/Politics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
FundersFederation for the Humanities and Social SciencesUniversity of TorontoMemorial University of NewfoundlandGovernment of CanadaSocial Sciences and Humanities Research Council of CanadaCanada Council for the ArtsUniversité Laval
KeywordsPolitical sciencePublic administrationPolitical economySociology

Abstract

fetched live from OpenAlex

Communication, Strategy, and Politics is a groundbreaking series from UBC Press that examines elite decision making and political communication in today's hyper-mediated and highly competitive environment.Publications in this series look at the intricate relations among marketing strategy, the media, and political actors and explain how they afect Canadian democracy.Tey also investigate interconnected themes such as strategic communication, mediatization, opinion research, electioneering, political management, public policy, and e-politics in a Canadian context and in comparison to other countries.Designed as a coherent and consolidated space for difusion of research about Canadian political communication, the series promotes an interdisciplinary, multi-method, and theoretically pluralistic approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0090.018
Science and technology studies0.0020.003
Scholarly communication0.0070.004
Open science0.0040.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0330.027

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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2023
Admission routes2
Has abstractyes

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