Absorption Narratives: Jewishness, Blackness, and Indigeneity in the Cultural Imaginary of the Americas
Bibliographic record
Abstract
"In Absorption Narratives, Stephanie M. Pridgeon explores cultural depictions of Jewishness, Blackness, and Indigeneity within a comparative, inter-American framework. The dynamics of Jewishness interacting with other racial categories differ significantly in Latin America and the Caribbean compared with those in the United States and Canada, largely due to long-standing and often disputed concepts of mestizaje, broadly defined as racial mixture. As a result, a comprehensive understanding of Jewishness and the construction of racial identities requires an exploration of how Jewishness intersects with both Blackness and Indigeneity in the Americas. Absorption Narratives charts the ways in which literary works capture differences and similarities among Black, Jewish, and Indigenous experiences. Through an extensive and diverse examination of fiction, Pridgeon navigates the complex connections of these identity categories, offering a comparative perspective on race and ethnicity across the Americas that destabilizes US-centric critical practices. Revealing the limitations of US-focused models in understanding racial alterity in relation to Jewishness, Absorption Narratives emphasizes the importance of viewing the narrative of race relations in the Americas from a hemispheric standpoint."--
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".