Storytelling and cultural identity: Louise Erdrich's exploration of the German/American connection in The Master Butchers Singing Club
Bibliographic record
Abstract
To understand stories, one must understand the spirit of the stories and the spirit of the person and the family who is telling them. Above all, one must know oneself. For Louise Erdrich, the American writer of Mtis/Cree/Chippewa (also known as Anishinabe or Ojibwa) origin on her mother's side and German/ Jewish/Catholic heritage, on her father's side, this has meant a lifelong commitment to writing in order to maintain a sense of sanity and stability. For it is this mixed identity that continually confronts her with a sense of, as she describes it, unziemliches Verlangen, unseemly longing. Erdrich writes about the interaction between Natives and Europeans in her novels and as writer and storyteller she incorporates not one, but several cultural identities. In much of the research about Erdrich these German/American connections have been all but ignored. This article seeks to address that anomaly.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".