The Almost Accidental Archive and its Impact on Literary Subjects and Canonicity
Bibliographic record
Abstract
The essay will explore the influence of archival selection on the construction of the Canadian literary canon. The author will discuss her experience acquiring the papers of twentieth-century author and journalist Wilfrid Eggleston for Library and Archives Canada. Amongst the Eggleston material were manuscripts by his wife, Magdalena. Writing from the 1940s to the 1970s, Magdalena achieved little critical notice or publishing success. When the Eggleston fonds came to Library and Archives Canada, however, the decision was made to keep her writing as an example of an unsuccessful author’s work. This essay will attempt to answer some of the questions raised by this archival selection: does Magdalena’s inclusion in Library and Archives Canada’s holdings, alongside such prominent authors as Robertson Davies and Carol Shields, change her importance to Canadian literature? Will she one day be “reclaimed” as a great talent by virtue of her work being archived and available for researchers? Finally, does a national heritage institution hold any responsibility in the documentation of the careers of unknown writers?
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.061 | 0.061 |
| Scholarly communication | 0.029 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".