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Record W2739184933

Role of work-integrated learning in developing professionalism and professional identity

2012· article· en· W2739184933 on OpenAlexfundno aff
Franziska Trede

Bibliographic record

VenueCharles Sturt University Research Output (CRO) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
FundersUniversity of South AfricaTshwane University of TechnologyUniversity of SurreyUniversity of WaterlooFlinders UniversityUniversity of New EnglandMurdoch UniversityMassey UniversityUniversity of JohannesburgCentral Queensland UniversityAuckland University of Technology, New ZealandAustralian Catholic UniversityUniversity of Western SydneyUniversity of Waikato
KeywordsTransformative learningPedagogyIdentity (music)Professional learning communityNexus (standard)Context (archaeology)CurriculumProfessional developmentAccreditationSociologyPsychologyMedical educationMedicineEngineering
DOInot available

Abstract

fetched live from OpenAlex

There is an increasing focus on the student as the nexus of integrating classroom and workplace learning. In the university context students are learners and in the workplace context students are pre-accredited professionals and in both contexts they can be facilitators of peer learning. Student participation in professional roles through workplace learning experiences are opportunities for transformative learning that shape professional identity formation and a sense of professionalism. Drawing on a higher education literature review of professional identity formation and a case study that explored how professionalism was understood, talked about and experienced by lecturers and students, this paper explores the role of work-integrated learning and its place in the curriculum to enhance professional identity development and professionalism. (Asia-Pacific Journal of Cooperative Education, 2012, 13(3), 159-167)

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)0.007
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.010
Scholarly communication0.0080.004
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.126
GPT teacher head0.434
Teacher spread0.307 · 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

Citations171
Published2012
Admission routes1
Has abstractyes

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