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Record W2077266537 · doi:10.1108/13639511311329723

Improving police training from a cognitive load perspective

2013· article· en· W2077266537 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolicing An International Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsCarleton University
Fundersnot available
KeywordsPerspective (graphical)OriginalityInstructional designTraining (meteorology)Computer scienceTransferabilityCognitive loadTransfer of trainingValue (mathematics)Domain (mathematical analysis)CognitionLearning theoryKnowledge managementMathematics educationPsychologyArtificial intelligenceMultimediaSocial psychologyMachine learning

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to present a theoretical framework, which describes how police training programs can be developed in order to improve learning retention and the transfer of skills to the work environment. Design/methodology/approach A brief review is provided that describes training strategies stemming from Cognitive Load Theory (CLT), a well‐established theory of instructional design. This is followed by concrete examples of how to incorporate these strategies into police training programs. Findings The research reviewed in this paper consistently demonstrates that CLT‐informed training improves learning when compared to conventional training approaches and enhances the transferability of skills. Originality/value Rarely have well‐validated theories of instructional design, such as CLT, been applied specifically to police training. Thus, this paper is valuable to instructional designers because it provides an evidence‐based approach to training development in the policing domain.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.051
GPT teacher head0.399
Teacher spread0.348 · 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