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Student Work in VCC Libraries: From Mannequins in the Library to a Car on the Third Floor

2020· article· en· W3116656405 on OpenAlexaffvenueabout
Kristina Oldenburg

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsVancouver Community College
Fundersnot available
KeywordsOutreachWork (physics)StudioGeneral partnershipDowntownRedevelopmentLibrary scienceEngineeringComputer scienceVisual artsBusinessPolitical scienceArt

Abstract

fetched live from OpenAlex

A range of student creations decorates Vancouver Community College Libraries. These showcase products from trade and design programs. The downtown campus library features mannequins with clothing designed and created by students in addition to display cases of student-made jewelry. We also exhibit styled wigs from students’ trades skills competitions and framed illustrations from the digital graphic design and drafting programs. The Broadway library has a small study room that the college’s Automotive Collision and Refinishing (ACR) department created from a Smart Car. ACR students also paint our book trucks to practice different designs, techniques, and finishes. Displaying student projects makes the library space more visually interesting. Moreover, the ACR contributions are functional items for the library’s operation. Displaying student projects also facilitates library outreach with shop- and studio-based programs. Furthermore, the work aligns with VCC’s provision of experiential learning. This project report outlines a successful partnership for library outreach with instructional departments. It includes lessons learned about internal and external communication in project management and what we attribute our successes to for our related event planning.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.006
Scholarly communication0.0120.005
Open science0.0020.013
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0500.013

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 head0.176
GPT teacher head0.420
Teacher spread0.244 · 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 designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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Same venuePartnership The Canadian Journal of Library and Information Practice and ResearchSame topicDigital Storytelling and EducationFrench-language works237,207