MétaCan
Menu
Back to cohort
Record W2116429257 · doi:10.1504/eg.2014.063313

New digital media use and preferences for government: a survey of Canadians

2014· article· en· W2116429257 on OpenAlexaboutno aff
Christopher G. Reddick, Patricia A. Jaramillo

Bibliographic record

VenueElectronic Government an International Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsDigital mediaGovernment (linguistics)The InternetService (business)Public relationsInternet privacyBusinessDigital governmentNew mediaPublic serviceE-GovernmentAdvertisingMarketingPolitical scienceComputer scienceDigital transformationInformation and Communications TechnologyWorld Wide Web

Abstract

fetched live from OpenAlex

This paper examines the use of new digital media, and the preferences for this technology, utilising the 2012 Canadian survey by the Institute of Citizen-Centered Service (ICCS). Specifically, we examine what is currently being used, and the preferences and service expectations for this new digital media technology. The results of this study indicate that citizens are increasingly using new digital media, and they have very high service expectations in regards to timely updates of information and responses from government. The statistical models reveal that new digital media use was shaped by demographic factors, internet use, as well as privacy and security concerns. These results show that governments need to pay more attention to the use and preferences of new digital media, especially given the tight budgets they operate under, and think of strategies to serve the public better through this technology.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueElectronic Government an International JournalSame topicE-Government and Public ServicesFrench-language works237,207