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Record W2583077528 · doi:10.1136/medethics-2016-103933

Appealing to the crowd: ethical justifications in Canadian medical crowdfunding campaigns

2017· review· en· W2583077528 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Medical Ethics · 2017
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsAppealPublic relationsMedical ethicsPolitical scienceVisibilityEthical issuesInternet privacyLawEngineering ethics

Abstract

fetched live from OpenAlex

Medical crowdfunding is growing in terms of the number of active campaigns, amount of funding raised and public visibility. Little is known about how campaigners appeal to potential donors outside of anecdotal evidence collected in news reports on specific medical crowdfunding campaigns. This paper offers a first step towards addressing this knowledge gap by examining medical crowdfunding campaigns for Canadian recipients. Using 80 medical crowdfunding campaigns for Canadian recipients, we analyse how Canadians justify to others that they ought to contribute to funding their health needs. We find the justifications campaigners tend to fall into three themes: personal connections, depth of need and giving back. We further discuss how these appeals can understood in terms of ethical justifications for giving and how these justifications should be assessed in light of the academic literature on ethical concerns raised by medical crowdfunding.

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.

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)0.039
metaresearch head score (Gemma)0.179
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.179
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0050.001
Research integrity0.0060.028
Insufficient payload (model declined to judge)0.0010.001

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 head0.233
GPT teacher head0.450
Teacher spread0.217 · 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