The biomedical lab after COVID-19: cascading effects of the lockdown on lab-based research programs and graduate students in Canada
Bibliographic record
Abstract
In the early months of 2020, the spread of the new coronavirus SARS-CoV-2 shook the world, which responded with various measures of quarantine, social distancing, and mass shutdowns. These measures have affected Canadian graduate students, especially those in lab-based science research programs who have been unable to access their workplaces. As a period of reopening may be approaching, and universities and research centers discuss ways to allow for work to resume cautiously, a discussion on the long-term impact of the pandemic on graduate students is necessary. Many students expressed significant concern regarding motivation with the shift to online courses; financial stress in terms of personal debt, bills, and tuition fees; as well as disrupted education and career plans. Uncertainty over the current economic situation and potential research material shortages lead to worry related to the quality and quantity of results and the impact these may have on funding. Luckily, universities, hospitals, governmental agencies, and employers have implemented a variety of programs and financial aid to help students in these trying times. Assuring the continuation of such measures is critical, and graduate students’ drive to succeed will manifest in novel ways to tackle the challenges that the COVID-19 lockdown has imposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".