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Record W4415675515 · doi:10.70085/jtse.v4i2.234

"I Can Do This!"

2025· article· W4415675515 on OpenAlexaff
Hilary Schmidt

Bibliographic record

VenueJournal of Trauma Studies in Education · 2025
Typearticle
Language
FieldSocial Sciences
TopicEducational Philosophies and Pedagogies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsEducational equityEquity (law)Qualitative researchPerceptionPostsecondary educationInclusion (mineral)Value (mathematics)

Abstract

fetched live from OpenAlex

Complex trauma is a source of significant multidimensional inequality, including profound disruption to survivors’ educational trajectories. Nonetheless, educational researchers have not previously engaged with survivors who study in open/online postsecondary settings, contradicting the key principle of collaboration within a trauma-informed approach. In response to this gap, this qualitative instrumental collective case study explored how adults with a history of complex trauma experience open/online postsecondary learning. Findings included participants’ struggles with executive functioning, regulating emotion, heightened perception of threat, re‑experiencing trauma, negative beliefs about the self, and navigating relationships. These trauma impacts affected not only participants’ course experience but also their experience of applying, registering, and accessing financial aid. Nonetheless, participants are highly skilled in managing impacts of their trauma and are driven to learn, placing the highest intrinsic value on education. Implications include enhancing equity and inclusion for survivors through pan-institutional 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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0090.006
Scholarly communication0.0050.008
Open science0.0010.007
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0600.037

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.

Opus teacher head0.125
GPT teacher head0.482
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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