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Record W3098310237 · doi:10.22329/celt.v13i0.6020

How can we help all students RISE?

2020· article· en· W3098310237 on OpenAlexaffvenueabout
Mathieu Chin, Mathew Dueck, Taylor Irvine, Owen Dan Luo, Ethan Pohl, Mariam Ragab, Hayat Showail, Tonya-Leah Watts, Tingting Yan, Enav Z. Zusman

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityQueen's UniversityUniversity of CalgaryMcGill UniversityDalhousie UniversityUniversity of TorontoLaurentian University
Fundersnot available
KeywordsScholarshipHumanitiesPolitical scienceNarrativeEmpowermentNarrative inquirySociologyArt

Abstract

fetched live from OpenAlex

This collection of short essays utilizes a narrative approach to present the RISE framework, with four pillars centered on promoting student Resilience, Identity, Strength in Scholarship and Empowerment. Each short essay aims to draw attention to the spectrum of challenges that students currently face during their higher education journeys in Canada and what innovative solutions have been or could be implemented to address these adversities in accordance with RISE. The application of the framework to evaluate and reorient learning environments in Canadian higher education holds immense potential to help all students grow as collaborative thinkers, partners, and leaders that will leave academic settings well prepared for their life as leaders in their community.
 
 Dans cet article qui rassemble de courts essais, nous utilisons une approche narrative pour présenter le cadre RISE en l’appuyant sur quatre piliers permettant de promouvoir la Résilience, l’Identité, la Solidité universitaire et l’Émancipation. Chacun des courts essais vise à mettre en relief, d’une part, l’éventail des défis auxquels les étudiants font face actuellement dans l’enseignement supérieur au Canada et, d’autre part, les solutions novatrices qui ont été mises en œuvre – ou qui pourraient l’être – pour s’attaquer à ces difficultés dans le contexte de RISE. L’application de ce cadre dans l’examen et la reconfiguration des environnements d’apprentissage de l’enseignement supérieur au Canada est pleine de promesses. Voilà qui pourrait aider tous les étudiants à devenir des penseurs collaboratifs, des partenaires et des leaders qui, lorsqu’ils quitteront l’université, seront prêts à agir comme des chefs de file de leur communauté.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.347
Teacher spread0.299 · 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 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

Citations0
Published2020
Admission routes3
Has abstractyes

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