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Record W2103683578 · doi:10.24124/c677/2014387

Referendum Resource Officers in the 2007 Ontario Referendum on Electoral Reform

2014· article· en· W2103683578 on OpenAlexaffvenueabout
Holly Ann Garnett

Bibliographic record

VenueCanadian Political Science Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsMcGill University
Fundersnot available
KeywordsReferendumPolitical sciencePublic administrationVotingOutreachPublic relationsGrassrootsLaw

Abstract

fetched live from OpenAlex

On October 10th, 2007, Ontarians overwhelmingly rejected a proposed change to their electoral system in a province-wide referendum on a new mixed-member proportional (MMP) system. Many commentators and academics blamed this failure of MMP on the quality of Election Ontario’s public education campaign, which was comprised of advertisements, an information hotline, a website, and public outreach activities. Elections Ontario’s public outreach element contained a unique program of grassroots education through local liaison officers. Elections Ontario chose to hire one Referendum Resource Officer (RRO) for each electoral district, who was tasked with providing referendum information through presentations and public meetings in their communities.
 
 This paper examines the feedback of one-third of these RROs collected through telephone and email interviews. Many of these RROs felt that the referendum education program fell short of its aim to provide local education on the referendum question and made suggestions as to the reasons behind the shortcomings of Elections Ontario’s referendum education campaign. They commented that their work was not supported by appropriate timelines, budgets and materials. In addition, many were displeased with the restrictions placed on RROs in efforts of keep the Elections Ontario campaign neutral. This case study supports previous referendum education and voting research that demonstrates that referendum education campaigns should not only provide timely and accessible information, but also encourage debate in order to provide citizens with the competence needed to make their “big decision.”

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.354
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations1
Published2014
Admission routes3
Has abstractyes

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