Insights into the impact of the pandemic on early career researchers: the case of remote teaching
Bibliographic record
Abstract
The study presents comparative qualitative findings from a longitudinal exploration of the impact of the pandemic on early career researchers (ECRs) from the sciences and social sciences. Using qualitative methodologies, it focuses on the increasing demands of remote teaching made on ECRs and the potentially negative effects these had on their research. The study also sheds light on ECRs’ country-specific teaching commitments and the extent to which these play a role in their assessment. Data comes from the first of three rounds of in-depth interviews, conducted with 177 ECRs from China, France, Malaysia, Poland, Russia, Spain, UK and US. The main findings, which are set against the published literature, were: a) over half ECRs teach and most of them are assessed on their teaching; b) there are significant differences between countries, with, for instance, French researchers hardly teaching and nearly all Polish researchers doing so; c) around a quarter of ECRs felt research was hindered during the pandemic because online teaching was increasingly demanding of their time; d) a preliminary analysis of ECRs’ gender-specific attitude to teaching in the pandemic-incurred new realities indicates that women experience more difficulties.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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".