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Record W4408611990 · doi:10.69520/jipe.v7i1.228

The Symbiotic Relationship Between the Classroom Experience, Work Integrated / Experiential Opportunities and Mentorship

2025· article· en· W4408611990 on OpenAlexaff
Cory Ross, Rajeev Chib, Isabelle Callahan-Nasser

Bibliographic record

VenueJournal of innovation in polytechnic education. · 2025
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsGeorge Brown CollegeCanadian Imperial Bank of Commerce (Canada)
Fundersnot available
KeywordsMentorshipExperiential learningWork (physics)PsychologyPedagogyMedical educationEngineeringMedicineMechanical engineering

Abstract

fetched live from OpenAlex

The world of education, training and employment has changed drastically over the last decade. The need for post-secondary institutions to reach even deeper to their respective partners is imperative. Long gone are the days when students enrol in programming and are matched with a work-related opportunity. The need for these opportunities has grown, and the mosaic of sophistication for the placement is much more complex. A missing part of the matching process of the student and their work-integrated/experiential learning opportunity is the bringing together of a mentorship program embedded in this learning equation. A learning opportunity can last for only a term, while a mentorship relationship can last for life.

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)0.019
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.035
Scholarly communication0.0220.011
Open science0.0020.024
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0090.002

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 head0.066
GPT teacher head0.378
Teacher spread0.312 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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