The School Librarian's Role in the Adoption of Open Textbooks
Bibliographic record
Abstract
Trends in adopting open educational resources (OER) in K-12 schools have opened numerous opportunities for schools to utilize their school librarians in new roles. The purpose of this study is to examine if schools are using their school librarians during the transition to OER use and if so, in what capacity. If not, why? In this study we used Rogers' (2003) diffusion of innovation theory, which describes the importance of change agents in the successful adoption of an innovation. We used thematic analysis and descriptive statistics to examine the research questions. Participants include a sample of representatives from school districts, which have signed on to be a #GoOpen school. Our results show that less than half of the schools use their school librarians in the OER creation and adoption process, but many participants acknowledgeschool librarians possessed a specialized skill set which could be beneficial in future OER creation projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".