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Record W3216625120 · doi:10.22215/cfice-2016-03

Closing the Loop: Community Engaged Pedagogy in Business Courses

2016· report· en· W3216625120 on OpenAlexaff
Leighann C. Neilson, Lindsay McShane

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsCarleton University
Fundersnot available
KeywordsClosing (real estate)Service-learningService (business)Loop (graph theory)PedagogyCommunity serviceSociologyPublic relationsPolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

Closing the Loop: Community Engaged Pedagogy in Business Courses is a CACSL and Carleton Raven’s Den-funded CFICE evaluation project that looks at the impact on Sprott School of Business’s community partners of adopting a community service learning approach to pedagogy. Over a number of years and across a variety of courses, Sprott has implemented projects ranging in duration and topic in order to facilitate a ‘practice’ perspective for the students in Sprott’s Bachelor of Commerce and Bachelor of International Business programs. Sprott has received lots of feedback from students, in the form of anecdotal accounts and more structured feedback exercises, and some feedback from community partners, but mostly the latter was limited to student performance during the actual project and anticipated benefits should the organization adopt the recommendations made by the student teams. Sprott therefore undertook this study to determine the impact their CSL projects made on community partners over a longer term. This project is still ongoing, with evaluations scheduled for the Fall/Winter term from 2016 – 2017.

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)0.022
metaresearch head score (Gemma)0.032
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0080.007
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0120.002

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 head0.168
GPT teacher head0.436
Teacher spread0.268 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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