Perceptions of Behavior Analysis in La Francophonie: Accuracy and Tone of Posts in an Internet Forum on Autism
Bibliographic record
Abstract
Applied behavior analysis (ABA) remains highly contested and under-utilized in many countries for the treatment of individuals with autism. One country where ABA remains particularly difficult to access is France. One potential problem is that parents often rely on online resources such as social media to identify interventions for their child. These sources of information may not accurately portray ABA or even openly disapprove of the approach. To examine this issue, we used data mining methodology to extract, categorize, and analyze 897 messages on ABA published in a popular French internet forum based on their type, tone, and accuracy. Although messages were generally accurate and approving of ABA, our results showed that one in three messages fully or partially disapproved of the approach and one in four messages contained some inaccurate information. Our analyses also indicated that parents were more likely to approve of ABA than individuals with an autism spectrum disorder. Finally, we found that the number of approving messages published in the internet forum decreased with time, especially over the last five years. Together, these results support the relevance of developing system-level approaches to dispel misconceptions about ABA in languages other than English.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".