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Record W4396670054 · doi:10.46743/2160-3715/2024.6167

Online Criticism of Parents After Child Accidents: A Reflexive Thematic Analysis

2024· article· en· W4396670054 on OpenAlexaff
Kelsi Toews, Jorden A. Cummings, Michelle McLean, Laura A. Knowles

Bibliographic record

VenueThe Qualitative Report · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsReflexivityCriticismThematic analysisPsychologyThematic mapSociologyQualitative researchGeographyLiteratureArtCartographySocial science

Abstract

fetched live from OpenAlex

When a child is harmed, parents frequently experience condemnation and blame from others. This blame is amplified online. Our online worlds reflect our offline ones, and this negative atmosphere toward parents can influence both parents themselves and societal expectations for parents. Previous research on parental blame has either directly asked people about their blame attributions or utilized hypothetical vignettes. Our thematic analysis expands on this research by analyzing unsolicited online comments left on news stories about two, real-world incidents of child harm: A child who fell into a gorilla enclosure at the Cincinnati Zoo, and a child who was killed by an alligator at Walt Disney World. We aimed to understand (1) What are people’s views and opinions of the parents of the child victims? and (2) Do these views and opinions differ between the CZ and DW events? Our results show three similar themes between these incidents: It Wouldn’t Happen to Me, Parenting Abilities and Actions, and Support, and two themes which differed between the incidents: Qualified blame/Sympathy and Punishment. The position of these findings within the parent blame literature, posited theoretical bases, and potential implications of this study are discussed within.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.362

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.093
GPT teacher head0.533
Teacher spread0.440 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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