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Record W3169719881 · doi:10.3233/ip-200301

The gendered digital turn: Canadian mayors on social media

2021· article· en· W3169719881 on OpenAlexaboutno aff
Katherine V.R. Sullivan

Bibliographic record

VenueInformation Polity · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaPoliticsPower (physics)Political scienceVariation (astronomy)Media coverageDigital mediaSociologyGender studiesMedia studiesLaw

Abstract

fetched live from OpenAlex

Women continue to occupy lesser positions of power at all political levels in Canada, although scholars still argue on the accessibility of municipal politics to women. However, no previous study has systematically examined the gender ratio of mayors across Canada, as well as their (active) use of social media platforms in a professional capacity. Using novel data, this study examines the variation in social media adoption and active use by gender outside of an electoral campaign. Results show that there is a higher proportion of women mayors who have a Facebook page, as well as Twitter and Instagram accounts and who actively use them outside of electoral campaigns, when compared with men mayors’ social media 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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0120.005
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.050
GPT teacher head0.310
Teacher spread0.260 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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