Call to Action: How can universities support doctoral and early career researchers during COVID-19 (and beyond!)
Bibliographic record
Abstract
When the COVID-19 pandemic hit the UK in March 2020, universities closed their doors with uncertainty over when they would reopen. In the early stages of lockdown, many doctoral and early career researchers (collectively, ECRs) felt their institutions had forgotten them.Vitae and the UKRI-funded Student Mental Health Research Network (SMaRteN) surveyed 5,900 ECRs across 128 UK universities at the end of April 2020, to establish the impact of lockdown on their work. While almost two thirds of respondents agreed that their supervisor/line manager had done all they could to support them, only 38% felt the same way about their institution. A quarter of respondents identified that their relationship with their university had worsened since the pandemic began. Right now, a key question is: what can universities do to support their ECRs?
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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 | 0.051 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.049 | 0.025 |
| Insufficient payload (model declined to judge) | 0.058 | 0.023 |
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".