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Record W4407640583 · doi:10.56238/sevened2025.001-013

THE LEARNING COMMUNITY AS A MEANS TO ENHANCE PROFESSIONAL SKILLS IN 3D PRODUCTION

2025· book-chapter· en· W4407640583 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

VenueSeven Editora eBooks · 2025
Typebook-chapter
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)PsychologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

This article explores the intersection between university pedagogy and 3D production, addressing the challenges of professional integration in this field and strategies to develop students’ skills. Partial results of an action research project are presented, focusing on the implementation of a learning community to support the development of 3D production skills in a Bachelor’s degree in media creation at a French-speaking university in Quebec, Canada. Analysis of data from a focus group interview with ten students who took the course revealed the contributions and limitations of the learning community. In addition, suggestions for future research in the area were identified. This study highlights the importance of innovative pedagogical strategies to prepare students for the ever-evolving job market, particularly in technology-driven sectors such as 3D production. The research also highlights the continuous need to adapt and improve teaching practices to better meet societal demands and promote graduates’ professional success.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.568
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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