The Tip of the Iceberg
Bibliographic record
Abstract
This chapter describes the efforts of a small, private college in Columbia, South Carolina to address the increased awareness of and need for traumasensitive practices across multiple settings through the development of a graduate degree existing solidly in the space between two different programs of study. Recognizing the need for trauma-informed education across multiple settings, faculty from the Division of Education worked alongside colleagues from the social work program in the Division of Social Sciences to co-create and co-sponsor the newly established master’ s in trauma-informed education (TIE). As the only partnership of its kind at the college, there has been a continued process of collaboration alongside the initial stages of implementation as the co-directors and faculty make sense of their data and determine next steps. The chapter begins by situating Columbia College’ s TIE program within a brief review of the literature and theoretical foundations. In the main sections, there is attention given to program logistics, course content, and pedagogical decisions. Based on feedback from both students and instructors, several changes have been made to the program since its start. Further, an initial analysis of the data and findings underscore the potential for future contributions to the research base on trauma-informed educational practices and more specifically in terms of the ways in which educators and other helping professionals are prepared to practice trauma sensitivity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".