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Record W3098667015 · doi:10.32866/001c.17977

Changes in Trip-making Frequency by Mode during COVID-19

2020· article· en· W3098667015 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFindings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMode (computer interface)Exploratory analysis2019-20 coronavirus outbreakSample (material)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Exploratory researchGeographyComputer scienceMedicineSociologyData scienceVirologyOutbreakPhysics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has had a profound impact on mobility in every country and region around the world. Recent studies help to illuminate some of the dimensions of change - however, the evidence is still scant in developing countries. The objective of this paper is to present an exploratory analysis of the changes in the frequency of trip-making by mode during the COVID-19 emergency in Bangladesh. The analysis is based on an online sample conducted during the pandemic, and the results confirm an overall loss of mobility, especially among younger people, in the form of reduced trip-making frequency by all modes. In addition, the results suggest that changes are not uniform across modes, and in particular the loss of mobility was more pronounced for bus, rickshaw, and CNG auto-rickshaw. In contrast, there was some adoption of walking during the pandemic.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.336
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it