Bibliographic record
Abstract
W riting a full-length biography of thomas douglas, fifth earl of Selkirk, has proved a far more difficult job than i had initially expected when i first undertook research for it in 1976.i had always conceived of biography as a fairly straightforward business.the subject in effect defined itself, and the events of the life had merely to be chronicled and explained.As i soon discovered, quite apart from the complexity of the chief character, a Selkirk biography was not all that simple.the first problem was a sea change in the nature of biography itself, brought about chiefly by the enormously detailed lives of literary figures.the amount of detail led the biographers to assume that their real task was to explain the subject's inner being, a quest for which they had enormous material.Unfortunately, there were great holes in the Selkirk Papers.the Public Archives of canada, as it was known when i began research on the life of Lord Selkirk in 1975, held a great cache of transcriptions from the family papers.the transcriptions ran to more than 20,000 pages.When examined more closely, however, these 20,000 pages contained a vast amount of material on the fur trade and events of the fur-trade war between Selkirk and the north West company, and not as much personal material dealing with his life as i had expected.i discovered that the transcriptions had been made by the Public Archives at the turn of the century from the vast horde of papers in the family muniment room at St. Mary's isle in kirkcudbright.these were subsequently microfilmed on twenty-one reels of microfilm, the form in which researchers now use them.the original instructions to the transcribers, local ladies of the P r e f A c e Selkirk_approved_jk.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.598 | 0.427 |
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".