Analysing gender equality in Barcelona through (spatiotemporal) segmentation
Bibliographic record
Abstract
Activity participation is influenced by many factors, such as the ones related to the built environment, but also to individual attributes. Herein we explore sequences of daily activities and travel employing techniques from the sequencing of events in the life course of individuals. Studying sequences of daily episodes (each activity and each trip) allows us to study the entire trajectory of a person's activity during a day while at the same time considering the number of activities, order of activities in a day, and their durations jointly. We applied this method to a sample of residents in the Metropolitan Area of Barcelona (RMB) in 2018, 2019, and 2020 Travel Surveys. We have focused on fragmentation analysis in activity participation, especially concerning gender, age, activity, and transportation mode. As expected, the survey from 2020 deserves a particular approach since activity patterns vary compared to surveys before the COVID-19 spread outbreak. In this respect, active transport shows to be particularly important that year. Overall, the results show that activity participation cannot be disentangled from gender, individual life-course, and the built environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".