Bibliographic record
Abstract
Researchers, educators, policymakers, and other education stakeholders hope and anticipate that openness and open scholarship will generate positive outcomes for education and scholarship. Given the emerging nature of open practices, educators and scholars are finding themselves in a position in which they can shape and/or be shaped by openness. The intention of this paper is (a) to identify the assumptions of the open scholarship movement and (b) to highlight challenges associated with the movement’s aspirations of broadening access to education and knowledge. Through a critique of technology use in education, an understanding of educational technology narratives and their unfulfilled potential, and an appreciation of the negotiated implementation of technology use, we hope that this paper helps spark a conversation for a more critical, equitable, and effective future for education and open scholarship.
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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 | 0.126 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.183 |
| Scholarly communication | 0.036 | 0.046 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".