The Virtue and Potential of Open Education: For Supporting Belonging, Transformation of Pedagogy, Linguistic Equity, and Climate Adaptation
Bibliographic record
Abstract
We are pleased to publish our second 2023 issue of the Open/Technology in Education, Society, and Scholarship Association (OTESSA) Journal.Although this was not a special call to focus on open, the works published in this issue all have a connection to open education, albeit from different angles.Across these research and practice articles, there was an underlying theme highlighting the important contributions that open education brings, or has the potential to bring, to learning and educational contexts.In the case of this issue, authors speak to what open brings to post-secondary education specifically. New Frontiers in Language Diversity at OTESSAWe thank all of our colleagues who have helped OTESSA grow from the beginning and we are pleased to be smoothing down the foundation of our organization, journal, and operations.Where OTESSA can continue to grow is in the inclusion and support of publication in languages other than English.In Canada, where the OTESSA Journal is based, there are two official languages, English and French.In this issue, we publish our very first French article by Catherine Lachaîne et Megan Cotnam-Kappel!We also thank our two colleagues for their patience as we had to pause the peer review process in order to locate an acting francophone editor.Achieving this goal of OTESSA Journal's first French publication surfaced the clear need for the editorial team to better prioritize inclusive practices for francophone authors, which is a priority and goal for 2024.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".