Recognizing Past and Present Experiences: Toward a Person-Oriented and Trauma-Informed Approach to Autism Research
Bibliographic record
Abstract
This paper draws on a Person-Oriented Research Ethics model in conjunction with a Trauma-Informed Approach to suggest a methodological framework and provide practical recommendations to guide research involving autistic people. Adverse and traumatic experiences are common in autism, and they add to autistic persons’ vulnerability in research. Participation in research can lead to further harm and re-traumatization if appropriate care is not taken to mitigate this possibility. Accordingly, this work analyzes the potential influences and ethical implications of negative experiences and traumatic histories for the autism research process. It also demonstrates the importance of adding a trauma-informed lens to a Person-Oriented Research Ethics model in research involving autistic individuals. Despite the high prevalence of trauma in autism, research addressing the implementation of a Trauma-Informed Approach or emphasizing the significance of applying trauma-informed principles to autism research remains notably scarce and overlooked.
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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 | 0.217 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.016 | 0.084 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".