Bibliographic record
Abstract
Abstract In January of 1891 Sir William Preece, Chief Engineer of the British Post Office, opined in a newspaper interview that ‘we have done as much with wireless telegraphy as is likely to be done’. Ten years later, on a windswept eminence in Newfoundland, Guglielmo Marconi clasped a telephone receiver to his ear and heard above the crackle of static a signal cast into the void at Poldhu in Cornwall 1,800 miles away. Preece had asserted—and many experts agreed with him— that ‘bridging the Atlantic’ was a pipedream, for ‘the curvature of the earth will send the waves out into space’. Preece, it must be said, seems to have enjoyed a remarkable record where prediction was concerned. When Alexander Graham Bell exhibited his first telephone Preece gave evidence before a committee of the House of Commons. His confident evaluation was: ‘Americans have need of this invention, but we do not. We have plenty of messenger boys.’ (Americans, by contrast, were on the whole cautiously optimistic. ‘One day’, said the mayor of Chicago after witnessing a demonstration of the instrument, ‘there will be one in every city.’ A Senator, on the other hand, when told that Maine would soon be able to speak to Texas, riposted, ‘What should Maine have to say to Texas?’)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".