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Record W2072867596 · doi:10.1016/j.sbspro.2013.12.295

Teamwork in Crossdisciplinarity

2013· article· en· W2072867596 on OpenAlexaffabout
Renée-Pascale Laberge

Bibliographic record

VenueProcedia - Social and Behavioral Sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTeamworkBachelorCoachingEngineering managementProcess (computing)EngineeringWork (physics)Session (web analytics)Knowledge managementPsychologyComputer scienceManagementPolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

The École Polytechnique de Montréal has integrated an approach of teamwork in its twelve engineering programs, in the bachelor's degree program since 2005. Students must take a compulsory 45 hour course on teamwork and are then accompanied with team coaching throughout the four years program, in all the engineering integration projects. These integration projects are generally ones in which, over a session, the students work in teams to conceive an engineering project. Within the framework of these projects (1 per year), a group communication specialist meets every team for approximately 60 minutes. This process promotes the transfer of the accumulated knowledge acquired in the teamwork course to their projects and helps identify the group processes at play in their teamwork dynamics. The objective pursued by this conference is to present the innovative formula used by the École Polytechnique de Montréal and to demonstrate by clear examples how it allows the development of skills in teamwork in all the scientific disciplines.

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)0.006
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0050.024
Scholarly communication0.0090.004
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.081
GPT teacher head0.371
Teacher spread0.291 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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