Impacto do teletrabalho nos padrões individuais de atividades e viagens: estudo exploratório com empresas e teletrabalhadores.
Bibliographic record
Abstract
Telecommuting can be considered a measure for travel demand management since it has the potential to reduce trips by replacing a face to face activity, which requires travel, by a virtual one, with the use of information and communication technologies.The objective of this research is twofold: first, to explore and understand the adoption of telecommuting by companies in São Paulo and, second, to identify and to measure the main impacts of telecommuting on individuals' activity-travel patterns.To achieve this aim, a comprehensive literature review was conducted, followed by two types of data collection efforts.First, in-depth semi-structured interviews were conducted with individuals responsible for Human Resources policies in ten companies adopting telecommuting or not.Second, a sample of telecommuters was recruited to answer to an online questionnaire and to provide detailed diary data for 7 days using smartphones, after which an in-depth interview was conducted.Interviews with Human Resources personnel revealed that potential benefits for companies and employees are the drivers behind the dissemination of telecommuting, although slow.As a yet unconventional practice, barriers continue to exist, particularly related to organizational culture.Data collection and analysis of telecommuters behavior shows that the congestion faced in the home-to-work trip is the main reason for adoption of telecommuting in São Paulo.Important differences in activity and travel behavior were observed between telecommuting and nontelecommuting days.While the number of participations and the time spent in non-work activities are relatively similar for the two types of days, a reduction occurred in the number of trips, total distance and time traveled on telecommuting days.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".