Assessing beach attendance and practices in a large coastal city. A case study in Marseille (France)
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.
Bibliographic record
Abstract
In large coastal cities, beaches are very important public open spaces. However, except in a few studies investigating interactions between uses and environmental beach quality, beach attendance and practices are generally poorly studied. In this context, this paper deals with a research initiative developed in Marseille (France), in order to: 1) assess beach attendance in summertime, 2) survey users’ practices, habits and preferences, and 3) interview municipal beach managers. Between 2016 and 2020, we collected data from 8 am to 8 pm on several summer days, following different time frames (three consecutive days, a full week, and the same weekday in July). We operated in three different beaches, one being studied every year. Attendance was assessed hourly, and practices were evaluated through face to face questionnaires on the field. The results obtained present interesting findings on several aspects. They help to better understand beach attendance as a system within the city at various time scales and throughout different geographical locations. They also help providing guidelines to set up a more ambitious and complete system to monitor beach attendance and practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it