Alarming gap on girls’ sport contributes to low participation rates

TRANSGRRLS ARE NOT THE PROBLEM!

Flinders University

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Lead Author, James Kay, PhD student, College of Education, Psychology and Social Work, Flinders University

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Credit: Flinders University

Researchers at Flinders University say there is an urgent need to encourage more girls to participate in sports, following a new study that reveals a striking lack of research on girls' sport engagement.

A new study in Sport in Society journal set out to review existing data on interventions to engage female adolescents in organised sport, and to explore the different factors that influence their experience and decision making in sport.

“Despite a rigorous systematic search of more than 3,000 articles, only five (globally) were found to have specifically examined ways to increase female adolescent participation in organised sport, demonstrating that this is a woefully under-researched area,” says James Kay from the College of Education, Psychology and Social Work.

“The distinct lack of literature on female adolescent sport participation may go some way to explain why we see so many girls drop out of sport and never return,” he warns.

In Australia, women are under-represented in organised sport—as participants, coaches, officials, administrators, and board members—when compared to their male cohort.

Despite the numerous benefits of sports participation—such as improved cardiovascular health, enhanced self-esteem, and better mental health outcomes—a staggering trend of disengagement persists among female adolescents.

Recent data indicates a dropout rate of approximately 60% for girls aged 15 and older in Australia, demonstrating a significant disparity compared to their male counterparts, who see a 42% reduction in participation.

“Factors contributing to the dropout include low confidence, societal pressures, body image concerns, and a lack of understanding regarding the impacts of the menstrual cycle on sports participation,” says Mr Kay.

“Additionally, prevalent gender stereotypes often discourage girls from pursuing traditionally masculine sports, leading to further disengagement.”

The limited evidence-based research available shows that when interventions by schools or clubs take into consideration girls' cultural and social norms, there is a greater engagement and retention of female adolescents.

Importantly, once they are engaged in a sport program and feel comfortable and on an equal footing with boys, the need for tailoring of activities is reduced.

“We need to more carefully consider the factors that contribute to female adolescent sport participation and find ways to better engage this population,” he says.

“We need to see sporting offerings available to female adolescents that are equivalent in quality to that of males. This doesn't necessarily mean they need to be identical, but currently there is a wealth of organised sport options available to boys, and far less for girls as they progress through adolescence, resulting in a disproportionate rate of dropout.

“It is hoped that this review can provide a basis for more research in this area and also highlight some key elements that future participation programs may wish to consider and incorporate,” he adds.

The article, “Organized sport engagement interventions for female adolescents: a systematic review using the Youth Sport System” by James Kay, Sam Elliott, Sarah Crossman, Murray Drummond  and Jasmine M. Petersen was published in Sport in Society journal. DOI: 10.1080/17430437.2025.2460486

Acknowledgements: The primary author was supported by a joint Flinders University – Office for Recreation, Sport and Racing Research Scholarship for a PhD with the College of Education, Psychology and Social Work and SHAPE Research Initiative at Flinders University.

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Journal

Sport in Society

DOI

10.1080/17430437.2025.2460486 

Method of Research

Systematic review

Subject of Research

People

Article Title

Organized sport engagement interventions for female adolescents: a systematic review using the Youth Sport System”

Article Publication Date

6-Feb-2025

COI Statement

Acknowledgements: The primary author was supported by a joint Flinders University – Office for Recreation, Sport and Racing Research Scholarship for a PhD with the College of Education, Psychology and Social Work and SHAPE Research Initiative at Flinders University.

 

ChatGPT for birdsong may shed light on how language is wired in the human brain

Penn State

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Illustration of Bengalese finches singing.

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Credit: Zachary Jin

UNIVERSITY PARK, Pa. — Just like ChatGPT and other generative language models train on human texts to create grammatically correct sentences, a new modeling method by researchers at Penn State trains on recordings of birds to create accurate birdsongs. The results could improve understanding of the structure of birdsong and its underlying neurobiology, which could lend insight in the neural mechanisms of human language, the team said. A paper describing the research was recently published in the Journal of Neuroscience.

Much like how humans arrange words in a particular order to form a grammatically correct sentence, birds tend to sing sets of notes called syllables in a limited number of combinations. 

“Although much simpler, the sequences of a bird’s song syllables are organized in a similar way to human language, so birds provide a good model to explore the neurobiology of language,” said Dezhe Jin, associate professor of physics in the Eberly College of Science and lead author of the paper. 

For both humans and birds, finishing a sentence or song sequence often depends on what has already been said. For example, the phrase “flies like” could be part of an analogy as in the phrases “time flies like an arrow” or an indication of enjoyment as in “fruit flies like bananas.” However, mixing and matching what comes after “flies like” results in “time flies like bananas” or “fruit flies like an arrow,” which don’t make sense. In this example, the phrase “flies like” has what researchers call context dependence. 

“We know from our previous work that the songs of Bengalese finches also have context dependence,” Jin said. “In this study, we developed a new statistical method to better quantify context dependence in individual birds and start to understand how it is wired in the brain.”

The researchers analyzed previously recorded songs from six Bengalese finches, which sing about 7 to 15 syllables in each sequence. With the new method, the researchers can create the simplest models that accurately reflect the sequences that individual birds actually sing.

The models are similar to large language models in that they depict probabilities of what words — or in this case syllables — are likely to follow a particular word/syllable based on previously analyzed texts or song sequences. They are a type of Markov model, a method to model a chain of events. They are presented as a sort of flow chart that starts with a syllable that points to options for different syllables that could follow. The probability that a syllable might follow is indicated in the arrow between them.

“Basic Markov models are quite simple, but they tend to overgeneralize, meaning they might result in sequences that don’t actually exist,” Jin said. “Here, we used a specific type of model called a Partially Observable Markov Model that allowed us to incorporate context dependence, adding more connections to what syllables typically go together. The added complexity allows for more accurate models.”

The researchers’ new method creates a series of potential models that could describe an individual bird’s song based on recorded sequences. They begin with the simplest model, using a statistical test to see if a potential model is accurate or if it overgeneralizes and produces sequences that do not actually exist. They work through more and more complex models until they determine the simplest model that accurately captures what the birds are singing. From this final model, the researchers can see which syllables have context dependence.

“All six birds we studied had context dependent syllable transitions, suggesting this is an important aspect of birdsong,” Jin said. “However, the number of syllables with context dependence varied among the individual bids. This could be due to several factors, including aspects of the birds’ brains, or, because these songs are learned, this could be related to the amount of context dependence in their tutor’s songs.”

To begin to understand the neurobiology behind context dependence syllable transitions, the researchers also analyzed the songs of birds that could not hear.

“In these birds, we see a dramatic decrease in context dependence, which suggests that auditory feedback plays a large role in creating context dependence in the brain,” Jin said. “The birds are listening to themselves and adjusting their song based on what they hear, and the related machinery in the brain likely plays a role in context dependence. In the future, we would also like to map neuron states to specific syllables. Our study suggests that, even when a bird is singing the same syllable, different sets of neurons might be active.”

The researchers said that their new method provides a more automated and robust way to analyze not only bird song, but other animal vocalizations and even behavioral sequences. 

“We actually used this method with the English language and were able to generate text that is mostly grammatical,” Jin said. “Of course, we’re not trying to create a new generative language model, but it is interesting that the same kind of model can handle both birdsong and human language. Perhaps the underlying neural mechanism is similar too. Many philosophers describe human language, and especially grammar, as exceptional, but if this model can create language-like sentences, and if the neural mechanisms behind birdsong and human language are indeed similar, you can’t help but wonder if our language really is so unique.”

In addition to Jin, the research team included Jiali Lu, who earned a doctoral degree in physics at Penn State in 2023; Sumithra Surendralal, who earned a doctoral degree at Penn State in 2016 and is now at Symbiosis International (Deemed University) in India; and Kristofer Bouchard at the Lawrence Berkeley National Laboratory. Funding from the U.S. National Science Foundation supported this research.

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Journal

JNeurosci

DOI

10.1523/JNEUROSCI.0522-24.2024 

Method of Research

Experimental study

Subject of Research

Animals

Article Title

Partially observable Markov models inferred using statistical tests reveal context-dependent syllable transitions in Bengalese finch songs

 

Critical thinking training can reduce belief in conspiracy theories

University College Cork

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• 
  
• 
  
• A new study finds that training in critical thinking skills can be effective in counteracting conspiracy beliefs.

• Many well-established programmes for reducing people's belief in conspiracies have either no effect or a negative effect.

• The study is the first to directly compare different strategies to reduce conspiracy thinking. 

A new experimental study has found that fostering critical thinking can be an effective method to reduce people's tendency to believe in conspiracy theories.

Led by researchers at University College Cork (UCC), the study is the first to directly compare methods that are used to reduce people’s belief in unfounded conspiracy theories.

The study found that many well-established interventions have either no effect or a negative effect on participants’ ability to correctly reason about conspiracy theories.

Conspiracy theories have transitioned from fringe phenomena to forces shaping political discourse and public opinion worldwide. Although holding conspiracy beliefs can have a detrimental impact on personal health, social connection, public health and public democratic citizenship, little research has been conducted to testing the methods that could reduce conspiracy beliefs.

Identifying conspiracy theories

Researchers found that existing methods to reduce belief in conspiracy theories often encourage people to simply dismiss all conspiracies rather than discerning which information is likely to be true from the majority that is not.

Researchers identified a new approach that improves people’s critical thinking and allows participants to distinguish between plausible and implausible conspiracy theories more effectively.

The study is the first to assess how people use critical theory specifically for conspiracies, using a tool called the Critical Thinking about Conspiracies assessment (CTAC), rather than only measuring their belief in specific conspiracies such as the faking of the moon landings. The CTAC allowed researchers to see how people reason about conspiracy theories versus whether they merely believe in them, which gave researchers a better understanding of their underlying thinking process.

Critical thinking about conspiracies

Critical thinking skills and an analytical mindset are the most effective means of challenging conspiracy beliefs.

Cian O’Mahony, UCC School of Applied Psychology and study lead researcher, said: “Events like Watergate and Tuskegee Syphilis Study show us that sometimes conspiracies can happen. It is important that we are not just teaching people to reject everything that is labelled as a conspiracy theory. Our study introduces a new approach that encourages careful judgment and cautions against automatic scepticism. Our new intervention, which reminds people not to reject an idea just because it’s labelled a conspiracy, and discouraged blind scepticism, successfully helped participants better distinguish between plausible and implausible conspiracy theories.”

“Our results suggest that current techniques used widely by psychologists improve people’s critical thinking about implausible conspiracy theories but don’t help as much with plausible ones. As such, these interventions may be merely encouraging blind rejection of all conspiracy theories,” Cian said. Researchers recommend that future studies focus on both measuring discernment of conspiracy theories and design interventions that encourage discernment over blind skepticism.

This work was funded by the Irish Research Council, in partnership with Google, through the Irish Research Council and Google Ireland Online Content Safety Scholarship

ENDS

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Journal

Advances in Psychology

DOI

10.56296/aip00030 

Method of Research

Experimental study

Subject of Research

People

Article Title

True discernment or blind scepticism? Comparing the effectiveness of four conspiracy belief interventions

COI Statement

This work was funded by the Irish Research Council, in partnership with Google, through the Irish Research Council and Google Ireland Online Content Safety Scholarship.

 

Machine learning boosts accuracy of solar power forecasts

Institute of Atmospheric Physics, Chinese Academy of Sciences

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A solar power farm. 

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Credit: Tom Fisk

As solar energy plays an increasing role in the global power supply, ensuring accurate forecasts of photovoltaic (PV) power generation is critical for balancing energy demand and supply. A new study published in Advances in Atmospheric Sciences explores how machine learning and statistical techniques can refine these forecasts by correcting errors in weather models.

Weather forecasts are a key input for PV power prediction models, yet they often contain systematic errors that impact accuracy. Researchers from the Institute of Statistics at the Karlsruhe Institute of Technology examined different ways of improving these predictions by applying post-processing techniques at various stages of the forecasting process. Their study tested three strategies: adjusting weather forecasts before they enter PV models, refining power predictions afterward, and using machine learning to forecast solar power directly from weather data.

“Weather forecasts aren't perfect, and those errors get carried into solar power predictions,” said Nina Horat, lead author of the study. “By tweaking the forecasts at different stages, we can significantly improve how well we predict solar energy production.”

The findings reveal that post-processing enhances solar power predictions the most when applied to power forecasts rather than weather inputs. While machine learning models generally outperform traditional statistical methods, their advantage in this case was limited—likely due to the available input data. The study also found that including the hour of the day as a factor was crucial for accuracy.

“One of our biggest takeaways was just how important the time of day is,” said Sebastian Lerch, corresponding author of the study. “We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms.”

One promising approach bypasses traditional PV models entirely, using a machine learning algorithm to predict solar power directly from weather data. This method offers a practical advantage: it does not require detailed knowledge of a solar plant's design, though it does need historical weather and performance data for training.

The research opens the door for future studies to refine machine learning approaches further, integrate additional weather variables, and extend analyses to multiple solar plants. As renewable energy continues to grow, improving forecasting techniques will be key to ensuring a stable and efficient power grid.

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Journal

Advances in Atmospheric Sciences

DOI

10.1007/s00376-024-4219-2 

Article Title

Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning

 

Filipino scientists make aluminum transparent by using tiny acid droplets

INVISIBLE ALUMINUM TO AVOID TARIFFS 

Ateneo de Manila University

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Researchers from the Ateneo de Manila University and the Nara Institute of Science and Technology made transparent aluminum oxide (TAlOx) by applying microdroplets of acidic solution onto ordinary aluminum and applying a controlled electric current.

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Credit: Budlayan et al., 2025

Transparent aluminum oxide (TAlOx), a real material despite its sci-fi name, is incredibly hard and resistant to scratches, making it perfect for protective coatings on electronics, optical sensors, and solar panels. On the sci-fi show Star Trek, it is even used for starship windows and spacefaring aquariums. 

Current methods of making TAlOx are expensive and complicated, requiring high-powered lasers, vacuum chambers, or large vats of dangerous acids. That may change thanks to research co-authored by Filipino scientists from the Ateneo de Manila University. 

Instead of immersing entire sheets of metal into acidic solutions, the researchers applied microdroplets of acidic solution onto small aluminum surfaces and applied an electric current. Just two volts of electricity—barely more than what’s found in a single AA household flashlight battery—was all that was needed to transform the metal into glass-like TAlOx.

This process, called “droplet-scale anodization,” is not only simpler than existing manufacturing methods but also environmentally friendly, cutting down on chemical waste and energy use. The technique relies on a special effect called “electrowetting,” where an electric field changes the properties of a liquid droplet, allowing precise control over the anodization process.  

This new approach might make TAlOx cheaper and more accessible for applications in everything from touchscreens and lenses to ultra-durable coatings for vehicles and buildings. It could also lead to advances in miniaturized electronics, as scientists now have a way to convert metal surfaces into insulating, transparent layers on a microscopic scale.  

The breakthrough was published in the journal Langmuir by Marco Laurence M. Budlayan and Raphael A. Guerrero from the Ateneo de Manila University School of Science and Engineering’s Department of Physics; and Juan Paolo S. Bermundo, James C. Solano, Mark D. Ilasin, and Yukiharu Uraoka from the Nara Institute of Science and Technology Division of Materials Science’s Information Device Science Laboratory in Japan. 

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Journal

Langmuir

DOI

10.1021/acs.langmuir.4c03303 

Method of Research

Experimental study

Article Title

Droplet-Scale Conversion of Aluminum into Transparent Aluminum Oxide by Low-Voltage Anodization in an Electrowetting System