Bibliographic record
Abstract
The purpose of this paper is to interrogate the ways in which the selection of educational materials results in implications that impact access to these materials. This is necessary considering the evolving nature of educational materials offered by traditional publishers, and the increase in the availability of online learning materials, among those, open educational resources. I begin by reviewing the existing literature on emerging problems and barriers to learners’ access to educational materials including textbooks, online learning resources, and open educational resources. The findings from the literature review confirm that learners are now engaging with an increasingly complex ecosystem of educational materials, both print and digital, in a multitude of differing forms and formats, with various terms of use and durations of sustained access. Educators have a variety of choices to make when considering the educational materials to be used in their courses, and while fitness for purpose still dominates as the most important selection criterion, ease and persistence of access are becoming important considerations. A model which encapsulates the findings considering the variety of educational materials is presented alongside a discussion about the specific considerations for each.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".