Beyond COVID Chaos: What Postsecondary Educators Learned from the Online Pivot
Bibliographic record
Abstract
The online pivot has opened many people’s eyes to new possibilities and challenges in the postpandemic world. This article describes what five geographers in three different countries learned from the experiment and assesses how the lessons can be carried forward. One of the big surprises for some of us was the extent to which students were open to different ways of learning during the 2020–2021 academic year. It is clear that some students wish to continue their programs either partially or completely online, although it is also clear that students continue to enjoy field work. The online pivot also showed us that assessment needs to be reexamined, student stress levels need to be lowered, and inequities among students need to be addressed. There are challenges associated with online education across international borders. From a faculty perspective, we have found that nobody needs to be isolated from research opportunities and collaboration, but there are also limits on what we can do. There are growing threats to academic freedom, and we need to move faculty away from precarious employment. Finally, some of us learned the importance of work–life balance.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".