MétaCan
Menu
Back to cohort
Record W1824179100 · doi:10.24124/c677/20151196

Going Negative: Campaigning in Canadian Provinces

2015· article· en· W1824179100 on OpenAlexaffvenueabout
Alex Marland

Bibliographic record

VenueCanadian Political Science Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnticipation (artificial intelligence)PoliticsPolitical communicationAdvertisingPolitical scienceSubject (documents)BusinessPublic relationsLaw

Abstract

fetched live from OpenAlex

The study of political communication in Canada’s provinces suffers from an absence of pan-Canadian information. This descriptive article bridges the gap by documenting some observable trends. It submits that negative advertising is more intense in larger provinces than in smaller jurisdictions. Permanent campaigning is the new normal as electioneering ramps up in anticipation of a fixed date election. Provincial parties and citizens avail themselves of new technology by communicating with digital video, which is not subject to the same financial, technical, content or regulatory constraints as television. Similarities of political communication across Canada are noted, including copycatting of federal-level practices.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.014
Science and technology studies0.0050.003
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.387
Teacher spread0.324 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Political Science ReviewSame topicSocial Media and PoliticsFrench-language works237,207