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Record W187553105

Victoria University learning in the workplace and community: connecting partners, connecting fields, connecting learning

2012· article· en· W187553105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueVictoria University Research Repository (Victoria University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
FundersUniversity of South AfricaTshwane University of TechnologyUniversity of WaterlooUniversity of SurreyUniversity of Western SydneyGriffith UniversityFlinders UniversityUniversity of New EnglandMurdoch UniversityMassey UniversityUniversity of JohannesburgCentral Queensland UniversityAuckland University of Technology, New ZealandAustralian Catholic UniversityUniversity of Waikato
KeywordsGeneral partnershipOperationalizationScholarshipExperiential learningThe artsHigher educationService-learningSociologyPedagogyKnowledge managementPublic relationsPsychologyPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper outlines the innovative cross-discipline Learning in the Workplace and Community (LiWC) partnership model being trialled in the Faculty of Arts, Education and Human Development (FAEHD) at Victoria University (VU). The multi-faceted model responds to challenges arising out of a VU commitment to 25 per cent LiWC assessment across all courses. The model is based on the creation of holistic dialectical partnerships with external organisations in triangular learning relationships, consistent with the reconceptualization of twenty-first century learning. It responds to the challenges of developing and articulating authentic learning outcomes across a diverse faculty and scaffolds quality outcomes in scholarship of teaching and learning, graduate capabilities, flexible learning, and curriculum internationalization outcomes. The multi-faceted model also supports all stakeholder learning and maps learning outcomes, which supports the evaluation of progress. Lastly, this paper will also outline the operationalization model, which addresses resourcing issues, such as workload and time constraints, for all stakeholders.

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.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0150.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.005
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 head0.062
GPT teacher head0.340
Teacher spread0.277 · 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