Bibliographic record
Abstract
Business, public services and the economy: out of touch and out-performed In 1585, southwest of Greenland, three ships were caught in a gale. The mainmast of the lead ship, the Gabriel , was cracked and the top of the foremast torn off in the screaming winds. One of the ships went down. Men drowned. The gale lasted more than a week. The crew of one of the two remaining vessels mutinied, refused to continue the voyage, and sailed home to London, where they would claim to have seen the Gabriel sink. But the remaining ship carried on, north-west, towards the unmapped Arctic. The captain in charge of the expedition, Martin Frobisher, had been hired by the Cathay Company to sail in search of the North-West Passage, which the company hoped would create a trading route to China via Canada’s north coast. Having endured the loss of a further five men in a dispute with an Inuit tribe, Frobisher did discover a passage leading north-west. He returned to London and was commissioned for a second voyage. A great step forward. Well, not quite. Frobisher wasn’t the first to find that route. He wasn’t even the first non-Inuit to find it. Fishermen were there before him. (As one historian of Arctic exploration put it, they ‘stepped aside long enough to let the gentlemen discover the land, and then went back to fishing.’) Those fishermen could have saved Frobisher the arduous journey. They could also have told him the passage he’d found wasn’t a passage at all, but a 140-mile-long inlet (now called Frobisher Bay) that does not lead to China. Frobisher Bay is a dead end, which had sent sailors to their deaths and the Cathay Company into bankruptcy. Frobisher’s voyage is just one episode in a long history of financial and humanitarian disasters that could have been averted if the ‘establishment’ had been open to people from ‘the lower orders’. But the contributions of common seamen and fishermen ‘were thought by the upper classes not quite appropriate to the developing purposes of science’. Almost half a millennium later, things have changed. But some things haven’t changed as much as we might like to think. In 2005, after repeated safety warnings from workers in a BP refinery were ignored, a leak killed 15 and injured 500, blasted in the explosion or burned as a result of the air having caught fire.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.002 |
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".