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Record W846807817

The active recipient: participatory journalism through the lens of the Dewey-Lippmann debate

2011· book-chapter· en· W846807817 on OpenAlexaff
Alfred Hermida, David Domingo, Ari Heinonen, Steve Paulussen, Thorsten Quandt, Zvi Reich, Jane B. Singer, Marina Vujnović

Bibliographic record

VenueGhent University Academic Bibliography (Ghent University) · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizen journalismJournalismNewspaperTechnical JournalismPolitical sciencePublic relationsSociologyMedia studiesNews mediaLaw
DOInot available

Abstract

fetched live from OpenAlex

News outlets are providing more opportunities than ever before for the public to contribute to professionally edited publications.Online news websites routinely provide tools to facilitate user participation in the news, from enabling citizens to submit story ideas to posting comments on stories.This study on participatory journalism draws on the perspectives of writer Walter Lippmann and philosopher John Dewey on the role of the media and its relationship to the public to frame how professional journalists view participatory journalism.Based on semi-structured interviews with journalists at about two dozen newspaper websites, as well as a consideration of the sites themselves, we suggest that news professionals view the user as an active recipient of the news.Journalists have tended to adopt a Deweyan approach towards participatory tools and mechanisms, within carefully delineated rules.As active recipients, users are framed as idea generators and observers of newsworthy events at the start of the journalistic process, and then in an interpretive role as commentators who reflect upon professionally produced material.

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)0.013
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0090.030
Scholarly communication0.0230.016
Open science0.0020.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.087
GPT teacher head0.271
Teacher spread0.184 · 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 designNot applicable
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

Citations41
Published2011
Admission routes1
Has abstractyes

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