Bibliographic record
Abstract
The COVID-19 pandemic has had a profound effect on both education and research activities. A survey conducted within the geotechnical engineering and earth science academic communities between April 22 and 24 explored the variables that affect working efficiency and intellectual development during the pandemic period. We received 274 complete responses from faculty and graduate students in North America, Europe, South Korea, and Saudi Arabia. The four variables that correlate best with individuals’ perceived consequences of the pandemic are: setting daily goals, focus on academic tasks, time spent reading literature outside core research or on professional development, and commitment to exploring deeper scientific concepts. Overall, 28% of the respondents exhibit a positive outlook. For the other 72%, living with non-family members or with children, hindered access to needed materials, and excessive time spent with video entertainment exacerbated the perception of potential negative consequences of the pandemic. Observed percentages and trends are very similar across age, gender, living conditions and regardless of regional/national restrictions. Two complementary surveys addressed faculty choices for online education and student preferences. These results document the effective transition from in-person to online education using readily available technology, and highlight students’ preferences for in-person education followed by live online platforms; pre-recorded lectures emerge as the least preferable choice.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".