Bibliographic record
Abstract
At Face Value spins the tale of John White, a trusty Tory backbencher in Canada’s post-Confederation Parliament who was unusually sympathetic to women and Indigenous communities. Hewing closely to the archival record, it nevertheless diverges on one crucial point, reimagining White as a woman named Eliza McCormack. In this Canadian take on Moll Flanders , Don Akenson constructs a past in which people felt free to live in the gender of their own choosing, revealing the assumptions with which gender labels are freighted and the self-empowerment available to those who reject them. Following Eliza from her birth in 1832, amid the Irish cholera panic, At Face Value recounts her blacksmithing apprenticeship, a difficult passage to Canada, an unconventional marriage, and the peaks and valleys of her political career. In Eliza, Akenson offers readers a correction to the male-dominated historical record and an unforgettable literary heroine. Shortlisted for the Trillium Prize when it was released in 1990, this classic Canadian novel has only gained relevance in the thirty years since. At Face Value offers a window into the past and a mirror for the present.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.256 | 0.122 |
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".