Bibliographic record
Abstract
In the 1980s, anxiety about the extensive and ongoing conglomeration of the publishing industry led to the emergence of a movement of nonprofit publishers. It included counter-culture figures like Coffee House’s Allan Kornblum and Milkweed’s Emilie Buchwald, who got their start with boutique letterpresses; political and aesthetic activists like Arte Público’s Nicolás Kanellos, Feminist Press’s Florence Howe, and Dalkey Archive’s John O’Brien; and refugees from conglomeration like Fiona McCrae and André Schiffrin. Non-profits often defined themselves by their support for literariness, and which they depicted as under threat from commercial houses, which helped them gain support from private foundations, philanthropists, and government agencies like the National Endowment for the Arts. We discovered that these two different ways of structuring publishers’ finances—conglomerate and nonprofit—created a split within literature, yielding two distinct modes of American writing after 1980. This essay characterizes the two modes, explains how the split between them happened, and illustrates the significance of this shift for the rise of multiculturalism. We pay particularly close attention to the careers of Percival Everett and Karen Tei Yamashita.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".