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Record W4289775871 · doi:10.18192/cjmsrcem.v16i1.6453

Canadian Communication Studies and Experiential Learning

2018· article· en· W4289775871 on OpenAlexaffvenueabout
Sandra Smeltzer

Bibliographic record

VenueCanadian Journal of Media Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsWestern University
Fundersnot available
KeywordsInternshipPracticumExperiential learningPreparednessPublic relationsPedagogyPolitical scienceSociology

Abstract

fetched live from OpenAlex

In this article, I provide an environmental scan of experiential learning (EL) activities – internships, community engaged learning, co-ops, and practicum placements – offered by Canadian communication / media studies programs. These forms of ‘hands-on’ pedagogy can provide students with an opportunity to put their academic training into practice, to gain ‘in the field’ experience, and to collaborate with myriad community partners. However, the growth of EL must be contextualized within the neoliberalization of higher education in Canada, including concerns that universities are being cultivated as utilitarian conduits for job preparedness in a capitalist society. With the demand for EL opportunities unlikely to diminish, I argue that our field needs to proactively engage in determining the future of this form of pedagogy and ensure that it is ethical for all involved in the process. This discussion is informed by anonymous, semi-structured interviews I conducted with faculty, staff, students, and community partners associated with a selection of communication / media studies programs in three Canadian provinces. Further, I highlight recent shifts that have taken place in Ontario vis-à-vis EL as a case study to demonstrate the fast and furious policy changes underway at a provincial level.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.114
GPT teacher head0.402
Teacher spread0.288 · 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.

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

Citations1
Published2018
Admission routes3
Has abstractyes

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