Bibliographic record
Abstract
One pathway to creating more equitable schooling is through schools becoming trauma sensitive. Students exposed to trauma are more likely to struggle in school compared to their non-trauma-exposed peers. Changing the school environment allows trauma-exposed students more opportunities to access academics. This qualitative study explores the practices and strategies employed by one elementary school (K–5) to become trauma sensitive. Based on the data, five subthemes emerged that coalesce around the overarching theme of creating a caring community to achieve a trauma-sensitive school. For the purposes of this study, a caring community is defined as a group of people sharing a common workplace who have a true interest in the well-being of others in the community. The five subthemes include (1) the faculty’s commitment to creating a safe school, (2) intentional school design to foster support (covered in Ballin, 2022), (3) a commitment to engaging families, (4) a desire to make school fun, and (5) the principal’s support of the school community. By embracing practices aligned with trauma-sensitive schooling, this small school changed the learning environment to give more children chances for success despite current and past traumatic experiences.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".