Precarity and progression during a pandemic. Preliminary findings from a study of early career academics’ information behaviour during COVID-19
Bibliographic record
Abstract
COVID-19 has increased research, teaching and administrative pressures for all academics and, by doing so, exacerbated inequalities experienced by early career academics, who were already dealing with several sources of uncertainty in trying to establish their careers. This study sought to understand the experiences of the academics during the pandemic. We conducted semi-structured remote interviews with 18 participants (PhDs awarded in past 6 years), from a variety of countries; Canada, US, Australia, UK, New Zealand, and South Africa. Interviews were analysed using a reflexive inductive Thematic Analysis approach. Preliminary findings demonstrate that the pandemic has disrupted information acquisition and sharing among ECAs. The increasing amount of incorrect and irrelevant information disseminated by universities, alongside the de-prioritisation of information that is particularly valued by these academics (e.g., information related to professional development and career development) has led some to avoid information.The COVID-19 pandemic has further exacerbated the precarious situations faced. Universities need to acknowledge uncertainty, reduce information overload by providing relevant and useful information and provide useful information on and support for career progression.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".