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Record W2793059410 · doi:10.1080/1369118x.2018.1428655

Professionalization through attrition? An event history analysis of mortalities in citizen journalism

2018· article· en· W2793059410 on OpenAlexfundno aff
Ryan Larson, Andrew M. Lindner

Bibliographic record

VenueInformation Communication & Society · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
FundersConcordia University of EdmontonSkidmore CollegeUniversity of Minnesota
KeywordsJournalismAttritionScholarshipPopulationProfessionalizationOrganizational ecologyIncentiveSociologyPublic relationsPolitical scienceSocial scienceMedia studiesDemographyLawEconomicsMedicine

Abstract

fetched live from OpenAlex

Despite both scholarly and popular claims that citizen journalism (CJ) represents a growing democratizing force in the journalistic field, recent scholarship in the area has noted the decline of the organizational population of CJ. In this paper, we investigate how individual characteristics of sites and the dynamics of larger organizational population affect a CJ site’s risk of experiencing a mortality. Drawing on the largest sample to date of US-based English-language CJ sites, this study examines the risk of site mortality through an event history framework. Findings indicate that the strongest predictor of a site’s mortality is the age of the site, consistent with organizational population theory’s ‘liability of newness.’ We also find that for-profit and community-based sites have lower rates of site mortality, indicating that adopting legitimate conventions of journalism may serve as a protective buffer to site death. The results offer mixed evidence on whether CJ has become more professionalized via attrition.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.384
Teacher spread0.320 · 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 designQualitative
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

Citations5
Published2018
Admission routes1
Has abstractyes

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