Bibliographic record
Abstract
"Half Life," a novel in progress, includes three main settings and storylines: a fictional family's experience in Ukraine at the time of the Chernobyl disaster; Ontario's Ottawa Valley and the author's family from 1976 to 1989; and the life of Gilbert Labine, a prospector and mining executive in Canada during the first half of the twentieth century. "Half Life" posits uranium as the nexus between these apparently discrete fields: Labine discovered the mineral on Canadian soil in 1929 and built a refinery at Port Hope that continues to have political, environmental and health consequences. Canadian uranium went into the first atom bombs and, along with the domestic nuclear industry that grew, in part, out of Labine's discovery, remains one of the country's major exports. Radioactive waste from Port Hope, the nearby Chalk River facility of Atomic Energy and Control Limited, and high concentrations of naturally occurring uranium inscribed and effected the life of the author's family in the Ottawa Valley, where, coincidentally, Labine himself was also raised. And in Chernobyl, as well as in Hiroshima and Nagasaki (which will form yet another field in the finished novel), is found the most public and dramatic expression of uranium's divided nature as both promise and threat. "Half Life" takes this duality as both thematic and formal inspiration and uses the idea of mutation, perhaps uranium's most frightening potential, to connect characters, places and events and to put pressure on the division between fiction and non-fiction.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.021 |
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".