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Record W2766574264 · doi:10.15402/esj.v1i1.225

Community-Based Research and the Faith-Based Campus

2017· article· en· W2766574264 on OpenAlexvenueaboutno aff
Rich Janzen, Sam Reimer, Mark D. Chapman, Joanna Ochocka

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsFaithEthosSociologyParadigm shiftGeneral partnershipCommunity engagementHigher educationPublic relationsPolitical sciencePedagogyLawTheology

Abstract

fetched live from OpenAlex

Over recent decades a significant shift has been taking hold on campuses of higher education in Canada and around the world. It is a shift towards community engagement. In this article our focus is on the research aspect of community engagement, and explores how this shift towards community-based research is playing itself out on the faith-based campus. We provide examples of two Canadian faith-based universities (Crandall University and Tyndale University College & Seminary) who were involved in a two-year community-campus research partnership called “The role of churches in immigrant settlement and integration”. Reflecting on this experience we learned that, similar to other institutions of higher education, an intentional shift towards community-based research on the faith-based campus requires attention to both the internal and external drivers that support such a shift. We also learned that faith-based campuses have their own unique ethos and therefore have distinctive drivers that can be leveraged to support such a shift. While our learnings arise out of the experience of two participating universities, their applicability may be of interest to other faith-based campuses in Canada and elsewhere.

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)0.949
metaresearch head score (Gemma)0.766
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9490.766
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.9010.007
Scholarly communication0.0100.001
Open science0.0030.001
Research integrity0.0000.746
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 head0.286
GPT teacher head0.459
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations2
Published2017
Admission routes2
Has abstractyes

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