Bibliographic record
Abstract
Little did we know that we would be forced to do things differently because of a global pandemic when the idea for this special issue/section was developed in 2019! The Black Lives Matter movement in 2020 reinforced that action and not just talk is needed—we cannot continue to do things just because “we have always done it that way”. Effective change requires frequent, honest review and feedback, with revisions where needed to meet the desired outcome. All too often changes are made with the best intentions but result in unintended consequences (e.g., the cobra effect). The articles in this special issue section cover a variety of topics. The first two papers[1, 2] discuss the need for, and ways to achieve, inclusivity in the workplace. Diverse organizations are more successful, and male allyship can significantly advance culture change to reach equality for all underrepresented groups. The next four papers discuss moving past conventional teaching approaches and provide many ideas, including how polarity management maximizes team potential and leads to innovation[3]; real-world examples and team-based test retakes promote student engagement and success[4]; and open-ended laboratory problems[5] and multi-disciplinary capstone projects,[6] both with an industrial focus, promote learning and desirable engineering traits but require different course logistics. The final three papers discuss how to properly collect, analyze, and interpret data, not fear “failed” experiments, and avoid zombie ideas[7]; the benefits and challenges of disseminating results through open science[8]; and how to stimulate innovation in graduate students through open-ended exploration.[9] These topics are complementary, and, though the manuscripts were written independently, you will see many common threads throughout the articles. Thank you to all the authors who are clearly passionate about their work, as well as to the reviewers who provided thoughtful feedback. I would like to especially thank Prof. Suzanne Kresta for discussions and contributor recommendations in the development phase of this project. In the spirit of this special issue section, some articles reflect the personal perspectives of their authors and rely less on scientific evidence than traditional papers, but I hope that all the articles will encourage discussion, perhaps create some discomfort, and stimulate everyone to do things differently.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".