TRAUMA-INFORMED FLEXIBLE LEARNING: CLASSROOMS THAT STRENGTHEN REGULATORY ABILITIES
Bibliographic record
Abstract
<p style="margin: 0cm 0cm 24pt 36pt;"><span style="color: #131413; font-family: Times New Roman; font-size: medium;">This study explores the implementation of the first of three domains, increasing regulatory abilities, within a trauma-informed positive education (TIPE) approach with flexible learning teachers as they incorporated trauma-informed principles into their daily teaching practice. Trauma-informed teaching approaches have particular relevance for flexible learning settings, and can help meet the complex needs of students who have experienced violence, abuse, or neglect. This paper proposes that redressing a trauma-affected student’s regulatory abilities should be the first aim in this developmentally-informed TIPE pedagogy. Drawing from research with nine teachers working in trauma-affected flexible learning settings in a large metropolitan region, this study employs a qualitative appreciative inquiry action research methodology to explore the use of TIPE perspectives with their students. Under the domain of increasing regulatory abilities, four arising subthemes hold particular application for teacher practice and planning: rhythm; self-regulation; mindfulness; and de-escalation. These four subthemes are positioned as promising pathways to increasing regulatory abilities in students as they strive toward successful learning outcomes.</span></p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".