Bibliographic record
Abstract
Dr. Peter King has been active in digital documents and hypertext for almost thirty years. He was one of the founders of the Electronic Publishing conference series and of the ACM Symposium on Document Engineering, whose Steering Committee he now chairs. He holds the position of Professor Emeritus of Computer Science at the University of Manitoba in Winnipeg, Canada. He has recently held research positions at the University of Kent, UK and at LIRMM, Montpellier, France, and has worked at several other European and Canadian research institutes. Peter's work in document engineering covers several areas. He has performed individual and collaborative work on formalisms for multimedia document specification and on document description languages and systems, leading to the creation of several working systems. He has collaborated on work on hypertext, including the OPALES system and its successors, and on hypertext architectures supporting collaborative document usage and user communities. His joint work with Marc and Jocelyne Nanard won the ACM Engelbart award for best paper at the Hypertext 2003 Conference. Prior to developing his interest in document engineering and hypertext, he worked extensively in the area of programming language design and implementation. Peter King is a leader in computing education and educational standards. He is Director of Accreditation for CIPS, the Canadian Information Processing Society, which establishes and assesses industry recognized standards for postsecondary computing education in university and college institutions across Canada and internationally.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".