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Record W3013590693 · doi:10.11114/smc.v8i1.4734

Online Journalism: Crowdsourcing, and Media Websites in an Era of Participation

2020· article· en· W3013590693 on OpenAlexaff
Νίκος Αντωνόπουλος, Agisilaos Konidaris, Spyros E. Polykalas, Evangelos Lamprou

Bibliographic record

VenueStudies in Media and Communication · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsWorld Federation of Science Journalists
FundersUniversity of Cyprus
KeywordsCrowdsourcingJournalismTechnical JournalismPublic relationsSocial mediaPolitical scienceDemocracyWork (physics)Citizen journalismSociologyInternet privacyMedia studiesComputer scienceLawPoliticsEngineering

Abstract

fetched live from OpenAlex

The era of journalism and the participation of the readers on online media websites have changed online journalism. The research interest is now focused on removing the distinction between the publisher/entrepreneur and the journalist/user, with the ultimate goal of actively involving citizens in the journalistic process but also in the web presence of media websites. The evolution of technology, the deep media crisis and the growing dissatisfaction of the citizens, create the conditions for journalism to work with citizens, and in particular through citizen journalism and journalism crowdsourcing. This concept is a form of collective online activity in which a person or a group of people volunteer to engage in work that always involves mutual benefit to both sides. The main research question of this research concerns the analysis of the current situation regarding crowdsourcing, co-creation and UGC and the adoption of best practices such as crowdcreation, comments from the users, crowdwisdom, instant-messaging applications (MIMs) and crowdvoting used by media websites around the world. Very few media have tried to apply even nowadays, the proposed model of journalism, which this study is going to research. The results of the study shape new perspectives and practices for online journalism and democracy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.348

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.091
GPT teacher head0.326
Teacher spread0.235 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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