Bibliographic record
Abstract
Jeff Hancock is founding director of the Stanford Social Media Lab and is a Professor in the Department of Communication at Stanford University. Professor Hancock and his group work on understanding psychological and interpersonal processes in social media. The team specializes in using computational linguistics and experiments to understand how the words we use can reveal psychological and social dynamics, such as deception and trust, emotional dynamics, intimacy and relationships, and social support. Recently Professor Hancock has begun work on understanding the mental models people have about algorithms in social media, as well as working on the ethical issues associated with computational social science. Professor Hancock is well-known for his research on how people use deception with technology, from sending texts and emails to detecting fake online reviews. His TED Talk on deception has been seen over 1 million times and he’s been featured as a guest on “CBS This Morning” for his expertise on social media. His research has been published in over 80 journal articles and conference proceedings and has been supported by funding from the U.S. National Science Foundation and the U.S. Department of Defense. His work on lying and technology has been frequently featured in the popular press, including the New York Times, CNN, NPR, CBS and the BBC. Professor Hancock was a Customs Officer in Canada before earning his PhD in Psychology at Dalhousie University, Canada. He was a Professor of Information Science and Communication at Cornell prior to joining Stanford in 2015. He currently lives in Palo Alto with his wife and daughter, and he regularly does his best to stop pucks as a hockey goalie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".