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
In the last 20 years, we have witnessed an explosion in scholarship and popular media accounts about the experience of `mixed race' identity. Despite the increasing numbers of people who now identify as `mixed race', relatively little research has been conducted on how `mixed race' individuals consider this particular label of identity. Through qualitative, open-ended interviews with self-identified women of `mixed race' living in Toronto, this article interrogates attachments to the identification of `mixed race'. The article begins by examining the popular discourse surrounding `mixed race' identity, suggesting that the public imaginary positions the `mixed race' woman as `out of place' in the social landscape. It then explores how many women create cartographies of belonging by identifying as `mixed race', reading the label as a `linguistic home'. It can provide a way to identify outside of constraining racialized categories of identity. The article also points out that many of the same women in this study effectively challenge, contest and discard the identification, dependent on a myriad of factors.
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) | 0.007 | 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