Shifting Ground: Reflections on Research Relationships in the Time of Covid-19
Bibliographic record
Abstract
The Graduate Student Conference held at York University’s Graduate Faculty of Education in April of 2022 afforded all four authors occasion to share the impact of COVID-19 restrictions on our doctoral research. Each of our projects involved face-to-face research and required significant methodological adjustments and changes to research design in order to continue amidst a worldwide pandemic. Through the application of social adaptation theory and the practice of auto-ethnography, our discussion panel examined the significance of requisite reconfiguring of our projects on our research relationships. This paper offers post-panel reflective thoughts on how the discussion that took place during the panel has inspired further thinking about our research with drama students, international students, Rohingya refugee families, and refugee children, as well as our development as researchers.
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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 | 0.073 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.060 | 0.088 |
| Scholarly communication | 0.033 | 0.029 |
| Open science | 0.007 | 0.050 |
| Research integrity | 0.014 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 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".