Conversations with leaders: Sharing perspectives on the impact of and response to COVID‐19 and other crises
Bibliographic record
Abstract
Abstract Lessons learned during the COVID‐19 pandemic, through trial and error and sharing stories of successes and failures, have resulted in progress in the quest to resume what we refer to as normal or regular college life for students, faculty, and staff. However, it is doubtful that we will ever get back to the exact same situation that we were in prior to March of 2020, and that may not even be an appropriate goal for which to strive. We can learn from this pandemic and continuously improve what we do based on lessons learned rather than simply focusing on getting back to some sort of prepandemic “normal” state. This article and this entire edition of New Directions for Adult and Continuing Education are part of those efforts to document our experiences so we all can learn from them and move forward with that knowledge in mind.
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".