Research task (General Consumer Research):
What are some of the top computational social science labs in the US, in terms of undergrad research output and impact? Even if there are no readily available metrics on undergrad research output and undergrad research impact specifically, you should try to develop your own, with evidence included. How about for graduate research output and impact? Be specific - this shouldn't just be departments, but rather specific labs or research groups with PIs.

What are the top-paying jobs in industry for computational social scientists, and what kinds of backgrounds do these people come from? Name a few jobs, and for each, analyze a statistically significant sample to figure out whether they come straight from undergrad or do grad school first. Also, what they study and research in undergrad and grad school.

Evaluation rubric:
criterion: The response lists at least five named U.S.–based computational social science labs (not including whole departments or centers e.g., Lazer Lab,, Dan Jurafsky and the Stanford NLP Group, Blablablab, SALT lab)
weight: 5
axis: Explicit Criteria

criterion: The response lists departments or centers (e.g., computer science, MIT, math department,  Center for Computational Science & Engineering, Masters in Computational Social Scence) despite explicitly being excluded in the prompt.
weight: -4
axis: Explicit Criteria

criterion: The response lists labs only located inside the United States.
weight: 4
axis: Explicit Criteria

criterion: The response assigns weights to at least three unique quantitative metrics (e.g., number of publications, ratio of undergraduate students to graduate students, student satisfaction surverys, presence of undergraduate funding for research) of undergraduate research output or impact in building its undergraduate metric.
weight: 4
axis: Explicit Criteria

criterion: The response assigns weights to at least five unique quantitative metrics (e.g., publications, average time to graduation, average years funded, number of papers published in CHI per year) of graduate research output or impact in building its graduate metric.
weight: 5
axis: Explicit Criteria

criterion: The response provides graduate and undergraduate quantitative metrics explicitly present for each lab.  
weight: 4
axis: Explicit Criteria

criterion: The response uses at least one verifiable source or data link (e.g., Google Scholar, links to the lab in question, ACL Anthology, university-issued report) for every non-common-knowledge quantitative metric reported.
weight: 2
axis: References & Citation Quality

criterion: The report includes at least three distinct industry job titles (e.g., data scientist, computational social scientist, analyst, postdoctoral researcher) that employ computational social scientists.
weight: 4
axis: Explicit Criteria

criterion: The response cites salary data from a specific source (e.g., BLS, Glassdoor, LinkedIn, Anthropic website) to demonstrate it is among the top-paying roles for computational social scientists for every job title listed.
weight: 4
axis: Explicit Criteria

criterion: The response states the sample size used to analyze educational backgrounds for every job.
weight: 4
axis: Explicit Criteria

criterion: The response identifies high-paying industry jobs (e.g., machine learning engineer at Anthropic, Perplexity, computational social scienctist at Google DeepMind, OpenAI) for computational social scientists.
weight: 4
axis: Explicit Criteria

criterion: The response provides concrete evidence for undergraduate research output and impact (e.g., attendance at undergraduate conferences, analysis of undergraduate tracks at major conferences, publications by undergraduates, ratio of undergraduates present in a lab) at each lab.
weight: 4
axis: Explicit Criteria

criterion: The response lists the most common undergraduate majors and graduate fields (e.g., computer science, data science, linguistics, mathematics) for each job title listed.
weight: 2
axis: Synthesis of Information

criterion: The response includes one caveat expressing uncertainty where data are sparse or metrics are approximate.
weight: 2
axis: Implicit Criteria

criterion: The response's information about Principal Investigators is correct (e.g., name, affiliation, h-index, relationship to any other university centers).
weight: 4
axis: Implicit Criteria

criterion: The response names specific Principal Investigators (PIs) (e.g., David Lazer, Diyi Yang, David Jurgens, Dan Jurafsky) associated with the identified labs.
weight: 4
axis: Explicit Criteria

criterion: The response's analysis provide clear conclusions on whether job holders come directly from undergraduate degrees, rather than from graduate programs.
weight: 3
axis: Implicit Criteria

criterion: The computational social science labs discussed are from at least 3 different universities (e.g., Stanford, Berkeley, University of Maryland, University of Michigan). 
weight: 3
axis: Implicit Criteria

criterion: The response mentions alumni outcomes for at least one lab.
weight: 2
axis: Implicit Criteria

criterion: The response qualifies which conferences the lab primarily publishes in (e.g., CHI, EMNLP, NeurIPS, American Journal of Political Science).
weight: 2
axis: Implicit Criteria

criterion: The response provides a clear definition of computational social science.
weight: 5
axis: Implicit Criteria