Bibliographic record
Abstract
Introduction I was in one of those moods where danger is attractive. Hence I plunged from the start into a combination the outcome of which was exceedingly doubtful. For the gain of a pawn I risked to retard the development and to accelerate that of the opponent. Mr. Speijer wisely sacrificed also the exchange, and opened a concentrated fire upon my King; but once he missed the best continuation, and therefore lost quickly. Games of this character, where every move counts for much, are best suited to entertain spectators, and they are of great value for the ripening of the “position judgment”. He who relies solely upon tactics that he can wholly comprehend is liable, in the course of time, to weaken his imagination. And he is at a disadvantage against an opponent who tries to win through bold venture, yet does not step beyond the finely drawn boundary of what is sound. Emanuel Lasker (1908), quoted in Hilbert (2001), p. 5 Thus wrote world chess champion Emanuel Lasker in his Evening Post chess column in late December 1908, following his victorious third and final game in an Amsterdam match against the Dutch champion, Abraham Speijer. Having come to the city from Vienna, where he had been playing exhibition games the previous week, Lasker played Speijer in a pavillion in an Amsterdam park, watched by an audience of 150. The German beat the Dutchman in the first of three games but, to the delight of the Dutch audience, was held to a draw in the second. In the third game, shunning textbook play and avoiding safe continuations, Lasker won in twenty-seven moves.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".