Bibliographic record
Abstract
Some events from the recent headlines: A scientist retracts multiple papers after a bug in a homegrown program inverts several columns of experimental data, invalidating years of research. 1 A Canadian man discovers that he can access other people’s passport information over the web by altering the link to his own data. 2 A glitch exchanging information with the Social Security Administration causes Medicare to accidentally issue $50 million in refund checks. 3 Operations for querying, transforming, and integrating data lie at the heart of a growing number of software systems. Unfortunately, these systems are difficult to implement correctly using current tools: not only are the operations complex, correctness itself often hinges on intricate properties of the data and the way they are handled. The goal of my research is to develop tools that make it easy to build systems that are reliable, efficient, and maintainable. To that end, I am interested in designing programming languages with features like high-level syntax that elegantly expresses complicated transformations on data, precise type systems that track intricate correctness properties, and optimized implementations that make it possible to enjoy these benefits without sacrificing performance. Although my primary training is in programming languages, my approach to research is interdisciplinary: I seek out problems in areas where improved linguistic technology stands to provide substantial
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.338 | 0.165 |
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; the direct Gemma label and the distilled Codex classifier 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".