magdalena bennett

@maibennett.com

Research Scientist working on causal inference. The Anna Wintour of slide decks. If you have something to say, say it with a nice plot #rstats (Disclaimer: All opinions are my own)

If students are simply uninterested in the core bargain of a class like this, it’s hard to know what to do. You teach the material because you think it’s important they know it; they don’t give a shit, but want the credential; and the university wants their money. Your main option is “pretend”.

Andrew Heiss@andrew.heiss.phd · 4w ago

woof ≈80% of assignments this summer term have essentially been just raw chatgpt/claude output, including assignments that required designing stuff in illustrator/affinity/canva. The battle is lost for asynchronous online courses.

Just went to the Cubs - Mets game here in NY, and saw the dumbest ump call I’ve ever seen, when PCA was supposedly tagged out. WTAF. This country is profoundly divided, but one thing everyone can agree (even Mets fans) is that the call was bullshit. And still we won🙃. #GoCubs🐻

this is honestly an excellent celebratory and motivational speech from Mamdani at the Knicks parade. i'm ready to run through a wall, and i'm not even a Knicks fan

Validated against contemporaneous responses, a survey of 916 trainees and their career intentions finds a –22 percentage point change in academia intentions and a US-departure shift concentrated among foreign-born scientists, from Azoulay, Sadun, and Scur www.nber.org/papers/w35330

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New NBER working paper. We meta-analyze 82 RCTs of low-cost parent programs in 20+ countries. We use data on unwritten RCTs from funder records, RCT registries, author queries, etc. to estimate a model that adjusts for publication bias and characterizes the effect distribution for unwritten studies.

NBER working paper. 

Paper title: Characterizing the File Drawer: Evidence from a Meta-Analysis of Parent-Interventions Around the World

Abstract:  We conduct a meta-analysis of 82 randomized controlled trials across more than 20 countries to estimate the effects of low-cost, remote parental engagement interventions delivered through text messages, phone calls, and apps. We estimate a joint likelihood function that incorporates both written studies and unwritten studies identified through trial registries, funder records, research labs, evidence clearinghouses, and other sources. By also recording sample sizes for unwritten studies, the model estimates the distribution of standard errors, identifies write-up probabilities conditional on significance, and characterizes the file drawer by estimating effect distributions for written \textit{and} unwritten studies. Bias-corrected effects are 0.05 SD for test scores, 0.07 SD for grades, 0.05 SD for attendance, and 0.03 SD for enrollment. In the best-identified domain, test scores, statistically insignificant results are still written up at high rates. We also find that larger studies tend to estimate smaller latent effects, which could indicate that true effects are correlated with study precision, violating a common meta-analysis assumption. In smaller-sample domains, our approach helps identify selection probabilities by anchoring the absolute write-up rates. Finally, we estimate the value of additional RCTs to inform adoption decisions. Any single study estimate is unlikely to dissuade adoption because parent interventions have high marginal value of public funds. Instead, future research is most valuable when it can explain heterogeneity across settings.

Quarto 1.9 brings Typst even closer to LaTeX parity with 📖 Typst Books, ✍️ margins for figures, tables, and citations via the Marginalia package, and 🛜 new typst-gather tool for reliable, offline rendering. Details here: quarto.org/docs/blog/po...

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