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Record W4254163949 · doi:10.21432/t29q35

Audio-Visual Materials in an Integrated Literacy-Mechanical Skills Training Program for Young Unemployed Adults

2017· article· en· W4254163949 on OpenAlexvenueno aff
Orest Cap, Odarka S. Trosky, Barbara Wynes, Robin Cutts

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAudio visualVocational educationCurriculumClass (philosophy)PsychologyMathematics educationLiteracyPedagogyComputer scienceMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

This article describes how audio-visual materials were selected and usedfor a group of thirty unemployed young adults, ranging in ages (17-21), attending an integrated literacy-mechanical skills program. This program consisted of an initial two months of class activities followed by approximately seven months in the field and a final two weeks in class. The audio-visual materials which met Vander Meer's (1973) criteria of appropriateness, to reflect the curriculum and to elicit expected and desired behavior in the learner, were selected on the basis of the Von Mondfrans and Houser six step paradigm (1973) to relate to performance-based objectives. The selected materials did not necessarily reflect the very best nor the most recent in the field, rather they represented the best and most recent from those readily available. Evaluation through questionnaires, reports, observations and participant comments indicated that the audio-visual materials used in the program closely related to the three main functions of audio-visual materials in vocational instruction.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.382
Teacher spread0.359 · 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 teacher head, 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

Citations0
Published2017
Admission routes1
Has abstractyes

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