Bibliographic record
Abstract
Of all the legislative special measures intended to assist vulnerable witnesses, intermediaries have the greatest potential to help those with a communication need to give their best evidence. In the UK, 1% of people are estimated to have speech, language or communication problems sufficient to affect everyday functioning: this may be an underestimate (Enderby and Davies, 1989; Bryan et al, 1991). More than a million children suffer from speech, language and communication difficulties (Department for Children, Schools and Families, 2008); around 10% have a long-term speech, language and communication need (Law et al, 2000) or a clinically recognisable mental disorder (Office for National Statistics, 2005); and rates of childhood autism are around 1%, far higher than previous estimates (Baird et al, 2006). The origins of the intermediary role date back to 1955. In that year, Israel introduced the ‘youth examiner’, a social worker responsible for questioning children (Libai, 1969); South Africa passed legislation in 1977 enabling an intermediary to relay lawyers’ questions to children; a few more recent intermediary provisions in other parts of the world are not in active use (Henderson, 2012). Introduction of a scheme is under active consideration in New South Wales and South Australia (communications to the authors, March 2015) and is being piloted in Canada (www.access-to-justice.org). The intermediary concept was first proposed in England and Wales in 1987. Drawing on the Israeli model, legal scholar Glanville Williams proposed that a ‘child examiner’ relay lawyers’ questions to young witnesses while their evidence was videotaped before trial (Williams, 1987a, 1987b).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".