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Record W1962095449 · doi:10.24908/pceea.v0i0.5825

HIRED – Helping Industry Reach Engineers Directly An Initiative of Engagement – for Students and Employers

2015· article· en· W1962095449 on OpenAlexafffundvenueabout
Carolyn Geddert, Lynda Peto, Mathew Riesmeyer

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmployabilityAttendanceVariety (cybernetics)Engineering educationAcronymEngineeringPublic relationsBusinessMedical educationPsychologyPolitical scienceEngineering managementPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

In an effort to connect Engineering students with the diverse engineering community in Manitoba, University of Manitoba Engineering Society (UMES) and the faculty have collaborated and organized a regular weekly meeting called HIRED – an acronym for “Helping Industry Reach Engineers Directly”. Industry partners are invited and eager to present to an average of 100 students in attendance on a variety of topics. It’s about sharing information – about companies, career opportunities, industry insights, safety in the workplace, employment and employability skills, networking, and job search success. The benefits to students and industry partners are clear. Students are gaining a better understanding of the opportunities available within their profession and learn first-hand about what employers are looking for in an engineering graduate. Employers have the opportunity to meet their future employees and share with students the importance of professional skills and how to make the most out of their engineering education. These weekly meetings are strategically scheduled every Monday night from 5:30 through the fall and winter academic terms with a meal as part of the deal. It’s a winning combination for the students, employers, profession and the faculty.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.028
GPT teacher head0.270
Teacher spread0.242 · 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 designObservational
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
Published2015
Admission routes4
Has abstractyes

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