Online Criticism of Parents After Child Accidents: A Reflexive Thematic Analysis
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".