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Record W2169370213 · doi:10.1177/0741088311399710

Professional Citation Practices in Child Maltreatment Forensic Letters

2011· article· en· W2169370213 on OpenAlexaff
Catherine F. Schryer, Stephanie Bell, Marcellina Mian, Marlee M. Spafford, Lorelei Lingard

Bibliographic record

VenueWritten Communication · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsWestern UniversityUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsCitationCLARITYDocumentationRhetorical questionPsychologyForensic scienceMedicineLinguisticsLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Using rhetorical genre theory and research on reported speech, this study investigates the citation practices in 81 forensic letters written by paediatricians and nurse practitioners that provide their opinion for the courts as to whether a child has experienced maltreatment. These letters exist in a complex social situation where a lack of clarity exists as to which professional group (healthcare providers, police, social workers) is primarily responsible for gathering accounts of children’s injuries. Yet physicians need these accounts into order to compare them to actual injuries. The study documents the direct and indirect citations that occur in the letters, observes documentation strategies, notes the instances in which partial breakdowns in citation occur, and points to the linguistic factors contributing to these breakdowns.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.152
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.006
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.076
GPT teacher head0.310
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations7
Published2011
Admission routes1
Has abstractyes

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