Bibliographic record
Abstract
Dr. Bertram Gallant is an internationally known expert on integrity and ethics in education. She has consulted with or presented at high schools, colleges, universities and professional associations throughout the U.S. and around the world, including in Australia, Canada, Egypt, England, Jamaica, Mexico, and Singapore. She is the author of numerous journal articles and book chapters, as well as author of "Academic Integrity in the Twenty-First Century" (Jossey-Bass, 2008), co-author of "Cheating in School" (Wiley-Blackwell, 2009), editor of "Creating the Ethical Academy" (Routledge, 2011), and section editor for the Handbook of Academic Integrity (Springer, 2016). Tricia is a long-time leader with the International Center for Academic Integrity (ICAI), of which UC San Diego is an institutional member, and currently serves on their Board of Directors. In 2018, Tricia was the first recipient of the Tricia Bertram Gallant Award for Service presented by ICAI. You can follow Tricia professionally on Twitter or Facebook (@tbertramgallant).
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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.034 | 0.064 |
| Scholarly communication | 0.037 | 0.049 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".