Dealing With Isolation Using Online Morning Huddles for University Lecturers During Physical Distancing by COVID-19: Field Notes
Bibliographic record
Abstract
Isolation can affect our well-being negatively. To prevent the spread of the infection COVID-19, many workers, including university lecturers, are required to work from home. In order to maintain high levels of well-being and team cohesion, academics at the University of Derby Online Learning initiated a virtual huddle to briefly socialise and check on their colleagues’ well-being every morning. This piece of field notes reports the context (COVID-19 in the United Kingdom), the details of this morning socialization, the first-hand experience of attending this huddle, and possible applications. Perceived positive impacts of our huddles include better well-being, cultivating compassion in team culture, and enhanced team cohesion. These advantages can be also useful in student supervision, wider socialization with colleagues to counter the silo mentality, and other occupational sectors. Our field notes will be helpful for lecturers and other types of employees who work collaboratively yet in isolation during this uncertain and challenging time of crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".