“I wanted to live in that music:” Blues, Bessie Smith and Improvised Identities in Ann-Marie MacDonald’s <i>Fall on Your Knees</i>
Bibliographic record
Abstract
This paper explores the link between the improvisatory nature of blues music and resistance to socially prescribed expectations for gender and sexuality in Ann-Marie MacDonald’s first novel, Fall on Your Knees (1996). When Kathleen Piper, one of the main characters in the novel, leaves her home in Cape Breton in1918 to pursue a classical singing career in New York, she finds herself transfixed, and subsequently transformed, by a performance by Jessie Hogan (a fictional character clearly modeled on Bessie Smith), in large part because of her remarkable improvised vocals. Hogan’s performance points to the rich history of the great blues women of this time period, women who, through their songs, costumes, and improvised lyrics and melodies, explicitly and implicitly tackled issues such as domestic violence and poverty, and challenged normative ideas of black female identity and sexual orientation. This history provides a critical context for Kathleen’s growing sense of autonomy and sexual identity, and this paper argues that the representation of Bessie Smith in the novel (in the guise of Hogan) enables possibilities for improvising new social relations and sexual identities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".