Cultivating social well‐being: (Re)discovering the impact of positive relationships
Bibliographic record
Abstract
Long periods of isolation during the COVID-19 pandemic had drastic consequences for the social well-being in academia.We were forced to learn, teach, and collaborate in restricted social environments, develop new daily routines, and find ways to stay engaged in teaching and scholarly projects.The pandemic work experience demonstrated to us the significance of social well-being, that is, "building and maintaining healthy relationships and having meaningful interactions with those around you" (Boston University, 2022, social well-being) on our overall well-being and level of engagement in our work.We are reflecting on our experiences and the decisions we made in relation to social well-being as a faculty member and a graduate student who work at different institutions and have both changed workplaces and roles during the pandemic.The pandemic changed the way we think about personal and professional relationships and, as Canale et al. (2022) point out, has made the care for well-being "imperative" (p.730).We created well-being routines that helped us rediscover what can make our work so impactful, unique, and rewarding: cultivating positive relationships and being in dialogue with our students, colleagues, mentors, and other scholars. RESEARCH ON THE CONNECTION BETWEEN WELL-BEING AND LEARNINGWell-being is a multidimensional concept that reaches far beyond our physical and mental health.Well-being includes environmental, financial, occupational, intellectual, spiritual, as well as a social dimension.In fact, the social dimension of well-being, which includes our experiences of positive relationships and positive interactions, is the strongest predictor of our overall perception of wellbeing (Centers for Disease Control and Prevention, 2018).As instructors, it is critical to consider that learning and well-being are also interconnected.Our ability to engage in learning is affected by our
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".