An Act of Hospitality: From Clinical to Trauma-Informed Academic Support
Bibliographic record
Abstract
Higher education environments tend to sustain interpretations of student success that place the responsibility on students alone. This perspective, often described as deficit thinking, shapes educational responses into remedial ones. In this view, students who struggle do so because of poor study skills or habits. Academic support, then, fills the students with what they lack. This approach assumes that all students access learning in the same way, and that all students are equally able to make good academic choices. However, research on adverse childhood experiences (ACEs) finds that many students bring with them a history of trauma, which changes the way they learn and respond to new stressors. A trauma-informed approach recognizes the institution’s responsibility to acknowledge the impact of trauma and to design support according to trauma-informed principles. This organizational improvement plan examines the institution’s role in academic support, with a change plan designed for one Canadian university. The discussion is rooted in interpretive organizational theory and social cognition as an approach to change, with specific attention on sensemaking. Using the change path model and a servant leadership orientation, I develop a three-loop plan that employs a community of practice (CoP) as the mechanism for change. I suggest the initiation of the CoP, strategies to mobilize change through social and institutional learning, mechanisms for monitoring the change path, and communication strategies to encourage second-order change. With a metaphor of hospitality, I consider how to open the educational space for all students to enter and thrive.\nKeywords: academic support, trauma, servant leadership, interpretivism, sensemaking, community of practice, hospitality
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".