More Than a Fever: Toward a Theory of the Ethnic Archive
Bibliographic record
Abstract
During its 2008 annual meeting at mla headquarters, the committee on the literatures of people of color in the united states and Canada (CLPC) took up the question of archival work in the study of ethnic literatures. After much discussion of the various ways ethnic literatures are rendered “illiterate” or unreadable, the CLPC proposed a session titled “Practices of the Ethnic Archive” for the 2009 MLA Convention in Philadelphia. That session revealed, and for some of us confirmed, that scholarly discourse on the archive continues, for the most part, to ignore the ethnic archive as distinct from its white, European counterpart. Four of the five essays included here (Carr, Cruz, Kaufman, and Washburn) grew from the conversation the session engendered; the PMLA editorial board invited Nicolás Kanellos, founder and director of the project Recovering the U.S. Hispanic Literary Heritage, to participate in the discussion as well. We are grateful to the contributors for their insights about what the ethnic archive reveals and about the unintended consequences of applying to its holdings the theoretical practices informing archival studies writ large.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.079 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".