Innovation and Progress for the Future of Higher Education: An Introduction to Issue 15.1
Bibliographic record
Abstract
During times of personal and global hardships, when tests of resiliency and optimism for the future seem relentless, I have found a sense of renewed vigor and hope in the commitment that members of our SoTL community continue to demonstrate towards innovation and progress in education.Along with many others in our community, I believe that education is the key to tackling many of the most pressing world issues, and that we are at a juncture in time when our (in)action could steer our futures towards very different outcomes.This issue of CJSoTL, like those that precede it, is a testament to the dedication of our community towards advancing teaching, learning, and human experiences, which in turn can meaningfully elevate the trajectories of individuals, their families, and their communities.I'm grateful for the values, organization, efforts, and generosity of all the members of our SoTL community, and I'm honored to be a part of it.Special thanks to all who contributed to this issue, and to three members of our editorial team who have concluded their terms of service: our first Co-Editor,
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 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".