Trauma-informed practices in early childhood education: Contributions, limitations and ethical considerations
Bibliographic record
Abstract
While it should be obvious on moral grounds that abusing children in any shape or form is wrong, biological, medical and economic arguments have been necessary to bring attention to the long-standing impact of early childhood trauma. In particular, stemming from the mental health field, a trauma-informed approach seems to have become a privileged way to understand and attend to children exposed to an array of traumatic experiences. However, the introduction of such an approach is relatively recent and its implementation still needs to be explored. In this article, the authors describe some of the possible contributions and limitations of a trauma-informed approach to early childhood educators’ practice. They highlight the risks involved in privileging children’s socialization to the detriment of their subjectification and underscore the need to broaden dominant approaches to early childhood trauma by assuming an ethical responsibility towards children. To guide educators in the necessary endeavour of encountering each child as an infinite Other, the authors found inspiration in the work of Lithuanian-French philosopher Emmanuel Levinas.
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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 | 0.253 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".