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Blending Storytelling with Technology in the Professional Development of Police Officers

2012· book-chapter· en· W2476221269 on OpenAlexaff
K. H. Anderson, William Muirhead

Bibliographic record

VenueAdvances in mobile and distance learning book series · 2012
Typebook-chapter
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsOntario Tech UniversityYork University
Fundersnot available
KeywordsStorytellingFeelingRecallPsychologyMediationNarrativeSocial psychologySociologyArt

Abstract

fetched live from OpenAlex

Policing is a storytelling profession. Storytelling is a linguistic medium for the sharing of experiences, values, and culture. Organizations have a need to promote the sharing of experiences from senior to junior members. Organizations desire to ensure that proper values and culture are reinforced during this sharing process. Technology affords a tool for the sharing, and for the mediation of what is shared. This study focuses on a case where technology was used for the direct sharing of experiences. This sharing was done through the use of storytelling in the form of video presentations delivered in an e-learning course. Participants viewing these videos reported feeling a sense of engagement and immersion in the experiences of the teller. Participants reported that stories aid their retention and recall of the content of them. Other storytelling professions will benefit from using similar approaches.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.326
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations2
Published2012
Admission routes1
Has abstractyes

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