Bibliographic record
Abstract
What a tumultuous year 2020 has been.As I reflect on this year and what it has meant for academic integrity in Canada and beyond, there is no doubt that the world has changed in ways we cannot yet fully appreciate.For me, the year began with assuming the role of Co-Editor-in-Chief for the International Journal for Educational Integrity.I will return to this point later. ICAI Conference 2020I recall being at the annual conference of the International Center for Academic Integrity (ICAI, 2020a), held in Portland, OR, USA, from March 6 to 8, 2020.The conference provides an opportunity to connect with friends and colleagues from around the world.The Canadian Consortium Day, offered as a day-long workshop to the main conference, has provided Canadians with an opportunity to connect since its inception (McKenzie, 2018).I expect I am not alone when I say that it is the highlight of the conference for many Canadians.We were delighted when Jennie Miron from Humber College was named to the Board of Directors of ICAI, joining long-standing Canadian board member, Amanda McKenzie.The conference also included moments of sadness, such as when news of the passing of Robert (Bob) Clarke was shared.Clarke was known for his work with Thomas Lancaster, including coining the term contract cheating (Clarke & Lancaster, 2006).The two of them became a dynamic duo of research and presentations on the topic, impacting scholars, practitioners and policy makers around the world.For details on Clarke's passing, see Reisz (2020).During this year's conference, the state of California to the south, and the state of Washington to the north, both declared states of emergency due to the SARS-CoV-2 (COVID-19) coronavirus.I recall sitting in the Portland airport after the conference awaiting the flight home to Calgary when we learned that Oregon's Governor, Kate Brown, had declared a state of emergency hours before (Hyams et al., 2020).
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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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.024 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 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".