Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper contributes to debates over algorithmic discrimination with particular attention to structural theories of racism and the problem of “proxy discrimination”—discriminatory effects that arise even when an algorithm has no information about socially sensitive characteristics such as race. Structural theories emphasize the ways that unequal power structures contribute to the subordination of marginalized groups: these theories thus understand racism in ways that go beyond individual choices and bad intentions. Our question is, how should a structural understanding of racism and oppression inform our understanding of algorithmic discrimination and its associated norms? Some responses to the problem of proxy discrimination focus on fairness as a form of “parity,” aiming to equalize metrics between individuals or groups—looking, for example, for equal rates of accurate and inaccurate predictions between one group and another. We argue that from the perspective of structural theories, fairness-as-parity is inapt in the algorithmic context; instead, we should be considering social impact—whether a use of an algorithm perpetuates or mitigates existing social stratification. Our contribution thus offers a new understanding of what algorithmic racial discrimination is.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it