Completing a doctoral dissertation during a global pandemic: Lessons learned.
Bibliographic record
Abstract
In March 2020, when the World Health Organization declared a global health emergency, I was a doctoral student at the University of Calgary. I was about three-quarters of the way through the program and was in the early stages of data gathering for my dissertation. The interruption to my studies was sudden and abrupt.
 Fortunately, I was able to continue interviewing research participants after a six week pause, but in a manner dramatically different than planned. I was also able to lean heavily on technology to adapt to the new conditions. The topic of my dissertation was collecting faculty perceptions of the need and urgency for change in the publicly funded postsecondary education system. Ironically, my participants identified technology as a major force of change in their paradigm as well.
 While completing the writing of my dissertation, the results of my data analysis and new literature being published magnified the strength of my findings. In hindsight, I realize that the timing of my work bridged the pre and post-pandemic environments. It also happening in real time, at a pace that might be unprecedented.
 While the pandemic cannot be declared over, it has already become clear that the nature of academic research has been irrevocably altered and that the publicly funded post-secondary education system has been similarly impacted. The results of my research provide a clear view of some of those changing conditions and allows us to project some perceptions of the future of the system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".