Bibliographic record
Abstract
Since completing his ScB and ScM at Brown University in 1970 and his PhD in Computer Science at the University of Toronto in 1974, Frank Tompa has been on the faculty in Computer Science at the University of Waterloo. He has also worked for periods of several months at the Oxford University Press, Bellcore, Microsoft Research, the University of Toronto, and Stanford University. His teaching and research interests are in the fields of data structures and databases, particularly the design of text management systems suitable for maintaining large reference texts and large, heterogeneous text collections. He has co-authored papers in the areas of database dependency theory, storage structure selection, query processing, materialized view maintenance, text matching, XML processing, structured text conversion, database integration, data retention and security, and text classification. In 2005, the University of Waterloo and the City of Waterloo announced the naming of the road Frank Tompa Drive in recognition of Professor Tompa being one of those who "epitomize the energy and enterprise that characterize the University of Waterloo." In 2010, he was named a Fellow of the ACM for contributions to text-dominated and semi-structured data management.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".