Bibliographic record
Abstract
Hugh Garner was a British-Canadian writer, journalist, and editor. His fictional writings reflect on the experiences of marginalized individuals, echoing his own early experiences of poverty and unemployment. Garner and his family moved from Batley, England, to Toronto, Ontario, when he was six years old, and settled in the working-class neighbourhood of Cabbagetown. The Great Depression forced Garner to leave high school in search of work to support himself and his family. He held a wide variety of jobs, working as a bicycle messenger, a factory labourer, a newspaper copy boy, and even riding freight trains across North America to pursue short-term and seasonal farm jobs. Politicized by these experiences, Garner volunteered in the Spanish Civil War, serving with the Abraham Lincoln Battalion of the communist International Brigades. After the Fascist victory in 1939, he enlisted with first the Royal Canadian Artillery and then the Royal Canadian Navy in World War II. With the war’s conclusion he returned to Toronto where he made a career of his writing, managing to support his wife, Alice Gallant, and their two children, Barbara and Hugh Jr, through the publication of short stories, novels, plays, radio and television scripts, and articles, and through his editorial work at Saturday Night Magazine and New Liberty Magazine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".