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Record W3095043942 · doi:10.15173/ijsap.v4i2.4203

Evaluating community-based research: Hearing the views of student research partners

2020· article· en· W3095043942 on OpenAlexvenueno aff
Mark Cullinane, Siobhán O’Sullivan

Bibliographic record

VenueInternational Journal for Students as Partners · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
FundersUniversity College CorkJohns Hopkins University
KeywordsGeneral partnershipParticipatory action researchDisadvantagedPopularityPublic relationsCitizen journalismCommunity-based participatory researchConstruct (python library)Qualitative researchMedical educationSociologyPedagogyPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Despite the increasing popularity in the academy of collaborative approaches to research, evaluating the impacts of Students-as-Partners (SaP) initiatives has thus far received less systematic attention. This paper presents an evaluation of a participatory community-based research project where academics partnered with 15 mature students in a socio-economically disadvantaged estate in the south of Ireland to co-construct a household survey and conduct field research to gather the views of fellow residents on the regeneration of their area. The paper reports the findings of a subsequent qualitative, participatory evaluation of the student’s experience of this partnership with academics and its impacts. The findings illuminate some of the benefits and challenges of community-based staff-student research partnerships and points to the imperatives of aligning institutional, funder, and community participants’ capacities and objectives throughout the research cycle and the importance of evaluation to inform good practice in community-based research.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.032
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science 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 score0.646
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.919
GPT teacher head0.780
Teacher spread0.139 · 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

Labeled directly by 2 models reading the full record.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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