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Record W1664439139 · doi:10.48550/arxiv.cs/0109096

CyberCampaigns and Canadian Politics: Still Waiting?

2001· preprint· en· W1664439139 on OpenAlexaboutno aff
Tony Christensen, Peter J. McCormick

Bibliographic record

VenueArXiv.org · 2001
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsPolitical sciencePolitical economyEconomicsLaw

Abstract

fetched live from OpenAlex

The early election call in the fall of 2000 provided the perfect opportunity to study the impact the Internet has had on election campaigning in Canada. With the explosion of use the Net has seen since the 1997 general election, Canadian federal parties stood at the threshold of a new age in election campaigning. Pundits such as Rheingold (1993) have argued that the Internet will provide citizens with a way to bypass traditional media and gain unmediated access to each parties political message as well as providing a forum for citizens to engage the parties, and each other in deliberative debate. Through a longitudinal analysis of party web pages and telephone interviews with party staffers, we analyze the role the Internet played in the election campaigns of Canada's federal parties. Our findings indicate that the parties are still focusing on providing online features that talk at the voter instead of engaging them in any type of meaningful discourse. Most of these sites were exceptionally similar in their structure and in the type of content they provided. Generally, these sites served as digital archives for campaign material created with other media in mind and despite the multimedia capabilities of the Internet, these sites tended to be overwhelmingly text oriented. In line with Stromer-Galley's (2000) discussion of why candidates in the U.S. avoid online interaction, we also argue that little incentive exists to motivate parties to engage in any meaningful interaction with voters online.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.066
GPT teacher head0.325
Teacher spread0.259 · 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.

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

Citations2
Published2001
Admission routes1
Has abstractyes

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