MétaCan
Menu
Back to cohort
Record W2519640351 · doi:10.5130/ijcre.v9i1.4353

Fundamental researcher attributes: Reflections on ways to facilitate participation in Community Psychology doctoral dissertation research

2016· article· en· W2519640351 on OpenAlexaff
Renato M. Liboro, Robb Travers

Bibliographic record

VenueGateways International Journal of Community Research and Engagement · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsReflexivityParticipatory action researchPedagogyPsychologySociologyEngineering ethicsSocial science

Abstract

fetched live from OpenAlex

As novice researchers, Community Psychology doctoral students encounter fresh challenges when they attempt to facilitate participation by members of the community in their dissertation projects. This article presents the merit in adopting fundamental researcher attributes, which have been described in published academic literature as personal characteristics that facilitate participation by members of the community in research studies. The value of these researcher attributes is exemplified in the discussion of one of the author’s experiences in the early stages of his dissertation research process. This article also presents new researcher attributes for facilitating participation by community members that the author recognised after critical reflection on his experiences during the same research process. Cultural humility, shared vulnerability, reflexivity, methodological flexibility, academic assiduity and creative resourcefulness are researcher attributes doctoral students should consider adopting and developing if they intend to facilitate participation by members of the community in their dissertation projects.Keywords: researcher attributes, graduate students, doctoral dissertation, participatory research, participation continuum

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)0.059
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.010
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 head0.860
GPT teacher head0.690
Teacher spread0.170 · 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 designObservational
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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueGateways International Journal of Community Research and EngagementSame topicCommunity Health and DevelopmentFrench-language works237,207