Human dimensions in water crisis management: Gender bias in water manager appraisals and implications for water decision-making
Bibliographic record
Abstract
Climate change increases water crises’ frequency and intensity, requiring more effective solutions and water management. Environmental scholars have found gender-diverse teams make more sustainable, efficient, and equitable solutions. However, women remain under-represented in water management, hindering effective decision-making. Further, water crisis communications carry inextricable mortality reminders: In a water crisis, access to a life-sustaining resource is threatened. Terror Management Theory stipulates that these mortality reminders activate predictable human responses to assuage the anxiety from thinking about our own demise, responses that include strengthening ingroup identities and distancing from outgroups. These responses may exacerbate gender biases already present in homogeneous management contexts, potentially limiting effective water management outcomes. We empirically tested effects of (a) a standard mortality reminder, (b) a water crisis reminder, and (c) a painful but non-life-threatening control reminder on judgements of same- or different-gender water managers. Ambivalent sexism (a framework consisting of hostile and benevolent sexism subcategories) was included as a moderator variable, revealing significant interacting effects based on participants’ benevolent sexism levels. Benevolent sexism (BS) stems from the belief that women need to be protected by men, creating prejudiced behaviours that may appear protective but in actuality harm gender equity. We found (a) water crisis reminders evoked responses similar to the standard mortality reminders and (b) significant interacting effects emerged regarding existential threat, benevolent sexism, and decision-makers’ gender. Specifically, control group males rated the water manager more positively, regardless of gender; male participants higher in BS rated the woman water manager more positively, regardless of reminder condition; and female participants lower in BS rated the woman water manager less positively in the threat reminder conditions. Reasons for these outcomes are explained alongside implications for effective water management. Thus, water crisis communications, mortality reminders, and sexism can influence gender bias in water management, negatively influencing sustainable water outcomes.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".