Using Self-Affirmation to Persuade Male Engineers to Respect Female Engineers

Women are underrepresented in Science, Math, Engineering and Technology (STEM). Due to negative stereotypes, females in these fields are often treated with less respect from their male peers. In this study, we compared a “Gold-Standard” Contact intervention based on the best-known research-based evidence in prejudice reduction research to a Two-Step Persuasion intervention that affirms male engineers and then persuades them to respect women’s abilities in engineering, and compared these interventions to control conditions. This study tests which intervention (a) most effectively increases male engineers’ respect for their female peers and (b) can generalize this effect to other women. Both the Gold-Standard Contact and the Two-Step Persuasion intervention increased respect toward female peers with whom male participants had direct interactions. The Two-Step Persuasion intervention also increased respect toward another female engineer with whom they had less direct contact—a female engineering TA—as well as toward a new female they had never met, compared to the contact-based intervention and the control condition. These findings suggest that our Two-Step Persuasion intervention may best generalize male engineers’ increased respect toward female peers whom they had direct interactions to other women. These findings suggest that changing men’s respect for women can be an effective strategy to create a stereotype-safe social environment. Although future investigation is warranted, the current study is a promising first step in developing this intervention

Girls Are Good At STEM: Opening Minds And Providing Evidence Reduce Boys\u27 Stereotyping Of Girls\u27 STEM Ability

Girls and women face persistent negative stereotyping within STEM (science, technology, engineering, mathematics). This field intervention was designed to improve boys\u27 perceptions of girls\u27 STEM ability. Boys (N = 667; mostly White and East Asian) aged 9-15 years in Canadian STEM summer camps (2017-2019) had an intervention or control conversation with trained camp staff. The intervention was a multi-stage persuasive appeal: a values affirmation, an illustration of girls\u27 ability in STEM, a personalized anecdote, and reflection. Control participants discussed general camp experiences. Boys who received the intervention (vs. control) had more positive perceptions of girls\u27 STEM ability, d = 0.23, an effect stronger among younger boys. These findings highlight the importance of engaging elementary-school-aged boys to make STEM climates more inclusive

A Model Setup for Mapping Snow Conditions in High-Mountain Himalaya

Seasonal snow cover is an important source of melt water for irrigation and hydropower production in many regions of the world, but can also be a cause of disasters, such as avalanches and floods. In the remote Himalayan environment there is a great demand for up-to-date information on the snow conditions for the purposes of planned hydropower development and disaster risk reduction initiatives. We describe and evaluate a snow mapping setup for the remote Langtang Valley in the Nepal Himalayas, which can deliver data for snow and water availability mapping all year round. The setup utilizes (1) robust and almost maintenance-free in-situ instrumentation with satellite transmission, (2) a freely available numerical snow model, and (3) estimation of model key parameters from local meteorological and snow observations as well as from freely available climatological data. Novel features in the model include the estimation of melt parameters and solid precipitation from passive gamma-radiation based snow sensor data, as well as improved parameterization and estimation of melt water refreezing (36% of total melt) within, and sublimation/evaporation (57 mm yr−1) from the snow pack. Evaluation of the model results show a reasonable fit with snow cover data from satellite images. As many of the high-mountain regions in central and eastern Nepal show high correlation (>0.8) with the estimated snow line elevation in the Langtang catchment, the results may provide a first-order approximation of the snow conditions for these areas too

Near real-time measurements of snow water equivalent in the Nepal Himalayas

Seasonal snow is an important component of the Himalayan hydrological system, but a lack of observations at high altitude hampers understanding and forecasting of water availability in this region. Here, we use a passive gamma ray sensor that measures snow water equivalent (SWE) and complementary meteorological instruments installed at 4962 m a.s.l. in the Nepal Himalayas to quantify the evolution of SWE and snow depth over a 2-year period. We assess the accuracy, spatial representativeness and the applicability of the SWE and snow depth measurements using time-lapse camera imagery and field observations. The instrument setup performs well for snowpacks >50 mm SWE, but caution must be applied when interpreting measurements from discontinuous, patchy snow cover or those that contain lenses of refrozen meltwater. Over their typical ∼6-month lifetime, snowpacks in this setting can attain up to 200 mm SWE, of which 10–15% consists of mixed precipitation and rain-on-snow events. Precipitation gauges significantly underrepresent the solid fraction of precipitation received at this elevation by almost 40% compared to the gamma ray sensor. The application of sub-daily time-lapse camera imagery can help to correctly interpret and increase the reliability and representativeness of snowfall measurements. Our monitoring approach provides high quality, continuous, near-real time information that is essential to develop snow models in this data scarce region. We recommend that a similar instrument setup be extended into remote Himalayan environments to facilitate widespread snowpack monitoring and further our understanding of the high-altitude water cycle

Near Real-Time Measurement of Snow Water Equivalent in the Nepal Himalayas

Seasonal snow is an important component of the Himalayan hydrological system, but a lack of observations at high altitude hampers understanding and forecasting of water availability in this region. Here, we use a passive gamma ray sensor that measures snow water equivalent (SWE) and complementary meteorological instruments installed at 4962 m a.s.l. in the Nepal Himalayas to quantify the evolution of SWE and snow depth over a two year period. We assess the accuracy, spatial representativeness and the applicability of the SWE and snow depth measurements using time lapse camera imagery and field observations. The instrument setup performs well for snowpacks >50 mm SWE, but caution must be applied when interpreting measurements from discontinuous, patchy snow cover or those that contain lenses of refrozen meltwater. Over their typical ~6 month lifetime, snowpacks in this setting can attain up to 200 mm SWE, of which 10-15 % consists of mixed precipitation and rain-on-snow events. Precipitation gauges significantly underrepresent the solid fraction of precipitation received at this elevation by almost 40 % compared to the CS725. The application of sub-daily time lapse camera imagery can help to correctly interpret and increase the reliability and representativeness of snowfall measurements. Our monitoring approach provides high quality, continuous, near real time information that is essential to develop snow models in this data scarce region. We recommend that a similar instrument setup be extended into remote Himalayan environments to facilitate widespread snowpack monitoring and further our understanding of the high-altitude water cycle