Bibliographic record
Abstract
Complex trauma is both a product and a source of significant multidimensional inequality, including profound disruption to survivors’ educational trajectories. Nonetheless, educational researchers have not previously engaged with adult survivors who study online, contradicting the key principle of collaboration within a trauma-informed approach. This qualitative instrumental collective case study explored how adults with a history of complex trauma experience postsecondary open/online learning. Findings included participants’ struggles with executive functioning, challenges regulating emotion and dealing with a heightened perception of threat, re-experiencing trauma, negative beliefs about the self, and difficulties navigating relationships. These trauma impacts affected not only participants’ learning and course experience, but also their experience of applying, registering, and accessing financial aid. Nonetheless, participants are demonstrably skilled in managing the impacts of their trauma and are driven to learn, placing the highest intrinsic value on education. Top priorities for the implementation of trauma-informed educational practices identified by participants included establishing safety; trust and transparency; and empowerment, voice, and choice. Implications include enhancing equity and inclusion for survivors of complex trauma through the implementation of trauma-informed educational practices in open/online postsecondary contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".