Exploring the Impact of COVID-19 on Greeting Behaviours in Education Through a Lens of Relational Engagement
Bibliographic record
Abstract
Various impacts of COVID-19 have been explored throughout the literature; however, no research has yet considered the impact of COVID-19 on greetings in education. This paper represents an attempt to address this gap. Using a lens of Relational Engagement, this paper explores the findings of a recent survey (n = 67) that asked how teachers have historically greeted students and how they will go about doing so upon return to a physical classroom space. Findings suggest that COVID-19 has significantly impacted teachers’ beliefs about greetings in the context of education, that teachers’ greeting behaviours are likely to change, and that it is possible if not likely that many teachers may experience various intra- and interpersonal conflicts when they next encounter students face-to-face.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".