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Record W2981267400 · doi:10.33137/ijournal.v4i3.33080

Impact of Promotional Events and Routes of Access on OurDigitalWorld’s Digital Newspaper Collection

2019· article· en· W2981267400 on OpenAlexvenueaboutno aff
Tiffany Luk

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperDigitizationAdvertisingWeb sitePolitical sciencePublic relationsBusinessWorld Wide WebEngineeringThe InternetComputer science

Abstract

fetched live from OpenAlex

A site traffic analysis study of 24 newspaper sites from OurDigitalWorld’s (ODW) digital newspaper collection and the Ontario Community Newspaper (OCN) aggregate site was completed between 2016 and 2018. Digital newspapers provide access to past and current heritage news, vital statistics, and newspaper clippings. Securing grants from government agencies is essential for local heritage organizations to launch, build, and maintain their digitization initiatives in order to ensure the continuity of local culture and heritage of the past to people of the future. Thus, assessing the impact of (e.g. site usage, Web presence) and routes of Web access to digital newspaper collections is important to determine promotional strategies to increase usage and awareness of the collections. Recommendations were made for ODW, public libraries, and heritage organizations to increase the Web presence and awareness of ODW’s digital newspaper collection and to educate users about the various uses of the newspaper sites. Keywords: Digital newspaper collections, site traffic analysis, web presence, user awareness, user education

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 score0.055
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.274
Teacher spread0.248 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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