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Public Engagement and Policy Entrepreneurship on Social Media in the Time of Anti-Vaccination Movements

2016· book-chapter· en· W2554639145 on OpenAlexaff
Melodie Yunju Song, Julia Abelson

Bibliographic record

VenueAdvances in public policy and administration (APPA) book series · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMeaslesMMR vaccinePublic spherePublic healthVaccinationPublic discourseSocial mediaPolitical scienceRubellaPublic engagementConfusionPublic relationsSociologyMedicinePsychologyVirologyPolitics

Abstract

fetched live from OpenAlex

North America has experienced a resurgence of measles outbreak due to an unprecedentedly low Mumps-Measles and Rubella vaccination coverage rates facilitated by the anti-vaccination movement. The objective of this chapter is to explore the new online public space and public discourse using Web 2.0 in the public health arena to answer the question ‘What is driving public acceptance of or hesitancy towards the MMR vaccine?' More specifically, typologies of online public engagement will be examined using MMR vaccine hesitancy as a case study to illustrate the different approaches used by pro- and anti-vaccine groups to inform, consult with and engage the public on a public health issue that has been the subject of long-standing public debate and confusion. This chapter provides an overview of the cyclical discourse of anti-vaccination movements. The authors hypothesize that anti-vaccination, vaccine hesitant, and pro-vaccination representations on the online public sphere is reflective of competing values (e.g., modernism, post-modernism) in contemporary society.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.054
GPT teacher head0.325
Teacher spread0.271 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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