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 essay draws together the disciplines of race theory, artificial intelligence, and phenomenology to engage the issue of racism as a learned phenomenon. More specifically, it centres on a comparison between robots and humans with respect to becoming racist. The purpose of this comparison is to illustrate the complex interconnections between racism, ontology, and learning. The essay begins with a discussion of race and racism that identifies both fundamentally as social realities. With this account, the essay draws on Hubert Dreyfus’ critical phenomenological work on artificial intelligence to outline several limitations for robots becoming racist. Next, the essay turns to the phenomenology of Merleau-Ponty as an ontological alternative for describing human beings and how racism is learned through habit and skill acquisition. In the end, it is suggested that this investigation not only provides an insightful glimpse into racism as a learned phenomenon, but also invites further discussion on how such racism may be confronted when it is viewed not simply as a cognitive issue, but rather as an issue of embodiment.
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