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Record W3025461698 · doi:10.15402/esj.v5i3.70366

Exchanges

2020· article· en· W3025461698 on OpenAlexaffvenueabout
Jayne Malefant, Penelope C Sanz

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of SaskatchewanMcGill University
Fundersnot available
KeywordsScholarshipConversationSociologyDisciplineMedia studiesCommunity engagementLibrary sciencePublic relationsPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

In the Exchanges, we present conversations with scholars and practitioners of community engagement, responses to previously published material, and other reflections on various aspects of community-engaged scholarship meant to provoke further dialogue and discussion. In this section, we invite our readers to offer their thoughts and ideas on the meanings and understandings of engaged scholarship, as practiced in local or faraway communities, diverse cultural settings, and in various disciplinary contexts. We especially welcome community-based scholars’ views and opinions on their collaborations with university-based partners in particular and engaged scholarship in general. 
 In this issue, we profile the perspectives of young scholars. Here we feature a conversation between Penelope Sanz, who recently obtained her Ph.D. in Interdisciplinary Studies from the University of Saskatchewan and who serves as the Journal’s pioneering managing assistant, and Jayne Malenfant, a 2018 Pierre Elliott Trudeau Scholar, Vanier Scholar, and Ph.D. Candidate at McGill University in the Department of Integrated Studies in Education. A young engaged scholar working with the homeless in Montreal, Jayne talks about her on-going study on how homelessness impacts young people’s education. She looks at the challenges of accessing educational institutional support, an issue, she says, close to her heart as she was once a homeless youth herself. She reflects on the need for academia to open more spaces for young researchers undertaking engaged scholarship to involve the homeless youths themselves in the search for solutions.

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)0.878
metaresearch head score (Gemma)0.738
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8780.738
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.7490.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0000.826
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 head0.295
GPT teacher head0.428
Teacher spread0.133 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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