Law and Adoption in the UK: A Conversation with Alice Diver
Bibliographic record
Abstract
abstract: In this conversation, Emily Hipchen speaks with Dr. Alice Diver (School of Law, QUB, N. Ireland) about some of the themes underpinning her publication, "'Monstrous Othering': The Gothic Nature of Origin-Tracing in Law and Literature" (November 2021). The conversation opens with a brief discussion of their own respective experiences as "mother and baby home" adoptees in the US and Canada in the 1960s before turning to an analysis of how the particular adoptee brand of "fearful otherness" is often represented—and indeed perpetuated—in certain works of "monstrous orphan" fiction. In respect of achieving meaningful sociolegal and cultural reforms, language is key. The debates surrounding the wording of Ireland's controversial Birth Information and Tracing Act (2022) highlighted how lingering prejudices still attach to the topic of adoption and to the need to find one's origins. Discriminatory barriers to access—and contact with genetic relatives—still exist: the use of labels matters, too, as the controversy over the use of the term "birth mother" within the legislation (since amended to "mother") also evidenced. Though mainly relevant to adoptee rights, and adoption law and policy, debates and discourse on language may also impact on other areas where losses of origins occur, such as surrogacy and international adoption.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.046 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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".