The impact of the COVID-19 pandemic on perceived publication pressure among academic researchers in Canada
Bibliographic record
Abstract
The phenomenon of "publish-or-perish" in academia, spurred on by limited funding and academic positions, has led to increased competition and pressure on academics to publish. Publication pressure has been linked with multiple negative outcomes, including increased academic misconduct and researcher burnout. COVID-19 has disrupted research worldwide, leading to lost research time and increased anxiety amongst researchers. The objective of this study was to examine how COVID-19 has impacted perceived publication pressure amongst academic researchers in Canada. We used the revised Publication Pressure Questionnaire, in addition to Likert-type questions to discern respondents' beliefs and concerns about the impact of COVID-19 on academic publishing. We found that publication pressure increased across academic researchers in Canada following the pandemic, with respondents reporting increased stress, increased pessimism, and decreased access to support related to publishing. Doctoral students reported the highest levels of stress and pessimism, while principal investigators had the most access to publication support. There were no significant differences in publication pressure reported between different research disciplines. Women and non-binary or genderfluid respondents reported higher stress and pessimism than men. We also identified differences in perceived publication pressure based on respondents' publication frequency and other demographic factors, including disability and citizenship status. Overall, we document a snapshot of perceived publication pressure in Canada across researchers of different academic career stages and disciplines. This information can be used to guide the creation of researcher supports, as well as identify groups of researchers who may benefit from targeted resources.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".