Bibliographic record
Abstract
In this chapter, the author introduces the concept of bi-racialized "institutions of violence" as an explanatory framework for the ways in which children, such as Ruby and Similola create family. Ruby and Similola construct personal narratives which address specific bi-racialized violence experienced in White English children&s;s homes. However, the testimonies of Akousa, Sarah, Yemi and Bisi, as well as the author&s;s own, also point to the lived psychosocial tyrannies of growing up metisse within one&s;s birth families. On both sides of the Atlantic there is continual debate over the repercussions of placing Black and metis(se) children with White families or family surrogates. This issue is particularly controversial when addressing the placement of metis(se) children, who can claim at least two heritages — and technical membership in both Black and White communities. As the tainted seeds of a forbidden union, Ruby&s;s and Similola&s;s fates seemed sealed even before they emerged as the sun-kissed daughters of White English and German mothers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".