Bibliographic record
Abstract
In 1966, Margaret Atwood and her long-time artist friend Charles Pachter produced a handmade collaborative text entitled Speeches for Doctor Frankenstein. The book is a series of poems by Atwood, spoken through the imagined voice of Doctor Frankenstein about the perils of creation, and was illustrated with Pachter’s evocative woodcuts. Only 15 copies of the work were produced. In 2012, Anansi Press issued an eBook version to coincide with the 40th anniversary of Atwood’s well-known guidebook to Canadian literature, Survival. The eBook of Speeches was promoted by Anansi, not only as an act of Frankensteinian creation (the physical book was literally hand-sewn and put together from bits and pieces, including human hair), but also as a work that distilled the “Gothic origins” of Canadian literature. The marketing of the eBook turned Canadian literature into a Gothic monster, taking readers back to the moment when, according to their promotional video, “a piece of Canadian cultural history is created,” here packaged for resale as a virtual and elusive cultural artifact. By launching the two works in tandem – Survival and Speeches – Anansi led readers to consider the ways that Canadian literature in 2012 was itself a monster looking back to the (textual) origins of its birth and survival.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.037 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".