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Record W2131599256 · doi:10.1504/ijpd.2012.051160

Developing professional competencies using a Living Lab approach: an exploratory study in the field of management education

2012· article· en· W2131599256 on OpenAlexaffabout
Mario Bourgault

Bibliographic record

VenueInternational Journal of Product Development · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsLiving labArtifact (error)Scope (computer science)EngineeringKnowledge managementEngineering managementSet (abstract data type)Field (mathematics)Engineering ethicsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

This paper presents an initiative in which living lab principles are applied to a teaching environment. Rather than develop an artefact or softwaresupported service, the aim was to develop professional competencies. In this sense, this study extends the scope of living labs beyond technological to individual development. The setting is a training exercise for engineers in a discipline that is associated with their practice: project management. In line with similar education experiments conducted at universities around the world, this case presents a Canadian initiative in which an environment was designed to allow students to interact in a quasi-real situation. A collaborative platform was set up so that distributed teams from two campuses could participate. The results are promising for both teaching and learning. Furthermore, the results can help advance the living lab approach as a viable method for knowledge creation, whether incorporated or not into a physical artifact.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.335
Teacher spread0.268 · 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 designQualitative
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

Citations6
Published2012
Admission routes2
Has abstractyes

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