Bibliographic record
Abstract
This paper analyses the semiotic features and errors of logic at work in racial profiling and racial reckoning. Anthropologists have long researched the concept of human “race”, including biological, linguistic, archaeological, and cultural approaches to this topic, and anthropologists now largely agree that “race” is principally a cultural concept, not a biological one. Yet practices of race involve inferences about physical attributes including human phenotype. While much attention has been given to understanding how race operates as a discursive form through which power is exercised, less analysis has been done on the “logic” of racial reckoning, and more generally, on the semiosis of race. What semiotic forms and ideologies are at work in racial practices? How do semiotic ideologies of race reproduce cultural distinctions and hierarchies? In short, how does race work semiotically and what can a semiotic analysis of race reveal? This paper examines a particular social practice – racial profiling – and the roles of iconicity and retroduction in it. I argue that iconicity is central to practices of race and that iconicity contributes to erroneous conditional probabilities and the retroductive reasoning that mistakenly serve to justify racial profiling.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".