Use of modern technologies by public transport passengers during the COVID-19 pandemic
Bibliographic record
Abstract
This paper presents the results of primary research involving a survey of Polish passengers during the COVID-19 pandemic. The survey was conducted in January 2022 using the CAWI method on a representative sample of N = 1,129 Polish adults aged 18 to 60. First, means of transport were classified by distance covered into urban, regional and interregional transport. Survey participants were asked a series of questions regarding the use of modern technologies in public transportation. Before that, however, the frequency of urban (agglomeration), regional, interregional (transregional) and private transport use during the COVID-19 pandemic was examined. More than half of respondents said they used modern technology across all modes of public transportation, and about a quarter said that while they had not yet used these technologies, they intended to do so. Only about 15% of respondents replied they did not use and did not intend to use modern technologies, regardless of the mode of transport. The aim of the paper is to examine the use of modern technologies by passengers in the era of the COVID-19 pandemic. Research results may help to influence passengers' purchasing decisions and to improve services offered by carriers in accordance with the objectives of long-term transport policy of both the European Union and Poland related to sustainable transport development.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".