Bibliographic record
Abstract
Rip currents are a global public health concern, which represent a hazard when swimmers become caught and panic or become exhausted when attempting to swim against the current and back to shore, leading to exhaustion. Studying rip currents from a social science perspective allows researchers to have a more comprehensive understanding of beach user risk. Current research highlights the disconnect between delivery and individual processing of rip current warning messages. This interpretation process is affected by social factors such as gender, age, or prior rip knowledge. Furthermore, a beach user may formulate opinions of safe or dangerous swimming conditions based on the actions of their peers. In this study, we will examine how both rip current warnings and the presence of other beach users simultaneously influence an individual’s decision to enter the water. A survey was sent to undergraduate students at a regional comprehensive University in Ontario, Canada. Results suggest individuals are unable to identify a rip current and decisions are influenced by the presence of fellow beach-goers. This study analyzes behavioural intentions and does not demonstrate action. Future work will assess the effectiveness of rip current warnings on beach sites and evaluate how beach users physically respond to warnings. Understanding how these variables work together will enable managers and communities to create the most effective warnings possible.
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 | 0.038 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".