Participation in OER Creation: A Trajectory of Values
Bibliographic record
Abstract
This paper provides an analysis of interviews with seven faculty members who engaged in creating Open textbooks funded by government grants at a university in Canada in 2018. Using four values—access and equity, community and connection, agency and ownership, and risk and responsibility—identified by Sinkinson (2018), McAndrew (2018), and Keyek-Fransen (2018), we traced the ways in which university faculty members’ understanding of Open changed through the process of Open Educational Resource creation. As a teaching support-focused unit, we explore ways to provide our faculty and instructors with meaningful opportunities to develop their Open pedagogy. These findings reconceive the way that Open Educational Practice can be promoted at our University and others. Instead of focusing solely on OER creation, our faculty started engaging in thinking through the different conceptions of Open educational practice and identifying which concepts resonated with them. By reframing the ways in which faculty thought about Open Educational Practices, we have been better able to address the ways in which we support them.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".