Bibliographic record
Abstract
This article is based on my personal reminiscences about the early days of computer chess tournaments, describing not only how different the technology was, but also that progress was steady and continues today in the broader field of Artificial Intelligence. The author was a participant in the 1st ACM computer chess championship (1970) and continued to compete well into the 1980s. Speaking for myself, I learned how to play chess in Junior High School (actually King Charles 1 Grammar School in Kidderminster, UK), but now only remember losing in a simultaneous game with C.H.O’D. Alexander (the UK Chess Champion) in 1950. In High School (Preston Grammar School) I played for the school’s chess team, who were undefeated in the 1954–55 school year. Naturally I played for the University of Nottingham (where I was studying Mathematics), and later for the Bedfordshire County team, before leaving to join Boeing, Seattle, in 1962. That said, I don’t think I was ever better than a Class A player. Basically, I have played chess all my life, and it has helped develop my problem-solving skills.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".