Will the Effects of COVID-19 on Commuting and Daily Activities of the University Students Be Maintained? Evidence from a Small Town in Sicily
Bibliographic record
Abstract
As many studies have already shown, the COVID-19 pandemic has had a great impact on the daily routines of people all over the world. University students form one of the most affected groups of people, since they have had to interrupt many of the activities that they usually perform, and have also had to get used to a new way of learning (e-learning). An important question that now arises is whether the changes that were identified within the pandemic period are to be maintained when the risk of being infected is eliminated. To this end, 537 university students of the Kore University of Enna, Italy, were surveyed. Their responses are analyzed descriptively, and an ordinal regression model is being developed to shed more light on the likelihood of retaining changes related with to transport mode choice. The results show that the likelihood of retaining all the changes when commuting and during daily activities is very high, demonstrating such willingness from the participants. Moreover, it has been shown that public transport has increased the probability of people being negatively affected by the pandemic in the long-term, and opportunities appear for increasing the modal share of active modes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".