Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it