Feeling Relieved: Creating a Positive Bathroom Field Culture in the Geosciences
Bibliographic record
Abstract
Fieldwork typically poses some level of disruption to regular bathroom habits, which can lead to discomfort, distraction, and, in some cases, serious health and safety risks.We all have a role to play in mitigating these hazards and ensuring field bathroom matters are not a barrier to participation.Geoscientists carry out fieldwork in a variety of settings, including urban environments, remote wilderness areas, and industrial sites, such as active mines.Field experiences may take the form of day trips or multiday excursions requiring camping or hotel stays.As such, fieldwork can pose some level of disruption to regular bathroom habits.Bathroom behaviors directly impact the physical and emotional well-being of field workers; negative outcomes range from minor discomfort to serious physical health problems.This topic is an important component of field safety but receives relatively little attention.Bathroom-related concerns can distract from participants' abilities to fully engage in fieldwork and may become a barrier to participation entirely.Although this is an issue that can affect anyone, it disproportionately impacts some groups, including those who squat to pee, menstruate, have chronic UT or GI conditions, have a physical disability, or are from cultural backgrounds with bathroom-related taboos or sensitivities.This likely contributes to the lack of diversity in the ranks of geoscientists.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".