The impact of a virtual doctoral student networking group during COVID-19
Bibliographic record
Abstract
Peer and cohort interaction are essential elements in building a sense of community for doctoral students, yet the restrictions placed on universities in the rapidly evolving COVID-19 environment challenged the ways both doctoral students and faculty approached their teaching and learning. In many environments, public health measures forced doctoral programmes to reconsider traditional delivery methods of supervision and peer learning. This study explores the value of a virtual doctoral networking group created to foster academic connection and peer learning during the COVID-19 global pandemic. Uniquely, the membership draws students from both traditional and applied doctoral programs that use different delivery modalities (online and in person) and includes students at various stages of their doctoral studies. Through the use of personal reflections, we created narratives that we analysed thematically using the Braun and Clarke method. Our findings challenge and extend the previous understanding of the cohort model of learning. We demonstrate that the benefits of the cohort model of learning can occur across programs and independent of the stage of progression in the programmes, in a virtual context. These benefits open opportunities to new ways of supporting doctoral students in a post-pandemic environment.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".