Fishing for difference: A case study of singularity and sensibility in critical literary life writing
Bibliographic record
Abstract
This paper draws upon a two-year study that examined how critical literary life writing helped a group of lesbian seniors negotiate identity. As we consider the notions of difference and singularity, building upon the earlier work of one of the authors, we analyze the work of one participant - an ex nun whose life’s work has made her remarkable in the eyes of the world. However, as oral historians (Portelli, 1981; Thompson, 2009) have found, epic stories of celebration and achievement can serve to limit the scope of life narratives. This can hold especially true for marginalized individuals (Boyd, 2008). We suggest that in this instance, structured practices of close writing opened up our participant’s narrative to lyric possibility, rather than the “grammars of consequence” (Zwicky, 2006).
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.049 | 0.039 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".