Culturally Relevant Booktalking: Using a Mixed Reality Simulation with Preservice School Librarians
Bibliographic record
Abstract
The role of school librarians is often overlooked in advancing a respect for cultural diversity among youth, yet librarians are in key positions to champion for social justice reform in educational settings. In this qualitative study, we examine preservice school librarians' experiences with booktalking multicultural literature in a mixed reality simulation environment, as a vehicle to introduce social justice issues. Our purpose was to explore the booktalking experience as a means of developing preservice librarians' understanding of culturally relevant pedagogy, a stance concerned with developing cultural competence and critical consciousness. Our findings revealed that preservice librarians gained different levels of understanding of culturally relevant pedagogy; yet, the experience provided them with an opportunity for engaging in critical reflection regarding personal bias and systemic racism in schools and literature.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".