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Record W6926687745 · doi:10.25384/sage.c.6276117.v1

A Look at the Difficulty and Predictive Validity of LS/CMI Items With Rasch Modeling

2022· other· en· W6926687745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
FieldEngineering
TopicSoil, Finite Element Methods
Canadian institutionsnot available
Fundersnot available
KeywordsRasch modelPredictive validityPopulationScale (ratio)PsychometricsItem response theoryRecidivismUncorrelated

Abstract

fetched live from OpenAlex

The current study aimed to provide data on the performance of items, dimensions, and the total score of the Level of Service/Case Management Inventory (LS/CMI), one of the most internationally used actuarial scales for the prediction of general recidivism in convicted persons. Using the full population of Quebec’s male incarcerated population evaluated between 2008 and 2015 with a 2-year follow-up (<i>N</i> = 15,961), results indicated that the predictive validity of the scale and its components was in line or better than effect sizes reported in other validation studies. A Rasch model was computed to obtain the difficulty parameter of LS/CMI items. Results indicated that items had varying levels of difficulty and covered the whole spectrum of the risk continuum. However, difficulty in Rasch was uncorrelated with the predictive validity of items, which casts a doubt on the applicability of some aspects of item response theory to actuarial scales.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.080
GPT teacher head0.311
Teacher spread0.231 · 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