Bibliographic record
Abstract
Abstract There is no single understanding of race to which everyone subscribes; it is a protean concept, accommodating various notions of human difference at the historical moments in which they emerge. Literary texts therefore do not represent a singular racial epistemology shared among Renaissance authors, readers, and audiences; rather, they demonstrate conflicting views about race, how it is determined, and what it tells us about individuals and groups of people. Scholars of Renaissance literature have explored what concepts of race do in specific cultural contexts, and the various ways racial differences were represented and understood before the advent of racial science in the late 17th century. Renaissance usages of the words race, raza, razza, and their linguistic equivalents denote, in their most benign sense, genealogy and lineage. Usages of these terms, nevertheless, locate individuals within genealogical and biological networks and insist that such networks are important to social organization. Race works as a tool for social organization that justifies varied types of domination, and in the Renaissance it drew from and informed established discourses of power—primarily religion, gender, and class. The concept bares vestiges of the word’s original definitions, asserting that certain aspects of identity are inheritable and inalterable, and then uses those aspects of identity to naturalize social hierarchies—White over Black, Christian over non-Christian, European over non-European. Race thus is a concept that intersects with cultural, somatic, sexual, and religious difference, and the Renaissance may be understood as a moment when race competes for dominance as a system of classification, justifying the rights of individuals and groups to rule over, disenfranchise, violate, and enslave others.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".