Reflections from a novice academic integrity researcher during COVID-19
Bibliographic record
Abstract
When I was accepted into a Doctor of Education (EdD) program, I could not have imagined that all of my data collection would occur during a global pandemic.I had enthusiastically submitted my research ethics application for approval in January of 2020 and was ready to begin interviews by the end of February.In March, when the pandemic became an exigent reality in Toronto, Canada, I began working exclusively from home and this included my doctoral work.At that time, I had one small collaborative research project underway, and my doctoral research about to begin.Both projects are related to academic integrity in Canada, and both stalled immediately.Now what!? COVID-19 restrictions posed several challenges for me as a student researcher, however, as I adapted, I began to realize that it also provided some unexpected opportunities.My doctoral research is surrounding contract cheating, also known as academic outsourcing (Awdry, 2020; Clarke & Lancaster
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 | 0.069 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.065 | 0.040 |
| Scholarly communication | 0.029 | 0.012 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.024 | 0.071 |
| Insufficient payload (model declined to judge) | 0.007 | 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".