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Record W4385240970 · doi:10.1002/nvsm.1812

Responsibilities to the story, campaign(er), and profession: Exploring important considerations shaping Canadian print journalists' coverage of medical crowdfunding campaigns

2023· article· en· W4385240970 on OpenAlexaffabout
Anika Vassell, Valorie A. Crooks, Jeremy Snyder

Bibliographic record

VenueJournal of Philanthropy and Marketing · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPopularityThematic analysisPublic relationsContext (archaeology)Scope (computer science)Political sciencePrint mediaQualitative researchSociologyMedia studiesNewspaperSocial scienceHistoryComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract Medical crowdfunding is growing in popularity in a number of countries, including Canada. In the crowdfunding context people write and share their own stories. This activity also intersects with conventional news media practices when journalists prepare stories about these campaigns. This intersection raises the question of what are print journalists' responsibilities towards covering human interest stories based on medical crowdfunding campaigns? In this qualitative analysis we explore this question through reporting on interviews conducted with 14 Canadian news media professionals. After transcript review, emergent themes were compared and contrasted across investigators to reach confirmation on the scope and scale of emergent themes. These themes were then contrasted against the existing literature and our research goals to aid in interpreting their significance. Thematic analysis of the interviews identified three key domains of responsibility for journalists, which are: to the story, to the campaign and campaigner, and to their profession.

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)0.009
metaresearch head score (Gemma)0.008
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 score0.175
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.070
GPT teacher head0.293
Teacher spread0.223 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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