Bibliographic record
Abstract
In contrast to the conventional 'neat history' that emphasizes solo male achievement in a singular mode, Martha Scotford once proposed 'messy history': a non-canonical, non-hierarchical, female-led approach that considers design as an open-ended feedback loop where women creators embrace multiple roles, such as collaborator, educator, consumer, and critic. The life of British illustrator and writer Olive Allen (married name Biller; b. 1879 England, d. 1957 Canada), nicknamed Bad Anna by her family for her 'artistic temperament', provides an opportunity to examine in what 'messy' ways private life and illustration career intermingled; and how practices like hers were devalued in the historical record. This chapter examines how Biller's oeuvre reflects the private school for girls that her family operated, annual family gatherings in Cornwall, and her art school training first as an Arts and Crafts acolyte of Herbert McNair and then as a graduate of the Slade School of Art. A 'messy history' analysis allows us to see the community behind Allen's illustrating 'for schoolgirls and other people' (as she put it). Allen's genteel rebelliousness ensured acceptability and mild progress for the girls she knew. It afforded 'Bad Anna' herself an identity, a market, and autonomy, while helping young readers mediate between Victorian mores.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".