Bibliographic record
Abstract
This paper introduces the concept of linguistic hijacking, the phenomenon wherein politically significant terminology is co-opted by dominant groups in ways that further their dominance over marginalized groups. Here I focus on hijackings of the words “racist” and “racism.” The model of linguistic hijacking developed here, called the semantic corruption model, is inspired by Burge’s social externalism, in which deference plays a key role in determining the semantic properties of expressions. The model describes networks of deference relations, which support competing meanings of, for example, “racist,” and postulates the existence of deference magnets that influence those networks over time. Linguistic hijacking functions to shift the semantic properties of crucial political terminology by causing changes in deference networks, spreading semantics that serve the interests of dominant groups, and weakening the influence of resistant deference networks. I consider an objection alleging the semantic corruption model gets the semantic data wrong because it entails those who hijack terms like “racist” speak truly, whereas it’s natural to see such hijacking misuses as false speech about racism. I then respond to this objection by invoking the framework of metalinguistic negotiation proposed by Plunkett and Sundell.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".