Assessing mental capacity in the context of abuse and neglect: A relational lens
Bibliographic record
Abstract
Mental capacity (MC) is increasingly recognized as one of the most complex and nuanced constructs that has legal, health and social care implications. Although the UN (2006) Convention on the Rights of Persons with Disabilities (CRPD) provides a strong foundation for asserting a rights-based approach that arguably calls into question the use of this construct entirely, a more moderate, practically-focused approach recognizes that mental (in)capacity continues to be invoked as the justification for over-ruling individual choice. In keeping with the philosophy of the CRPD then, and human rights-based principles more broadly, mental capacity must be (re)envisioned to achieve compliance with more rights-based, contextualized directives. This necessitates developing new approaches to the assessment of decision-making capability (DMC) - the process whereby mental capacity is evaluated in practice settings - that move beyond simplistic cognitive approaches to recognize capacity as a dynamic, socio-relational process. The purpose of this paper is to begin to identify the challenges and opportunities associated with this reconceptualization particularly in situations of abuse and neglect.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".