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Record W4312203061 · doi:10.15402/esj.v8i3.70755

Liberating Community-based Research: Rescuing Gramsci’s Legacy of Organic Intellectuals

2022· article· en· W4312203061 on OpenAlexaffvenue
José Wellington Sousa

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSociologyParticipatory action researchContext (archaeology)General partnershipConsciousnessCitizen journalismRubricSocial scienceEngineering ethicsEnvironmental ethicsPedagogyPolitical scienceEpistemologyAnthropologyEngineeringLaw

Abstract

fetched live from OpenAlex

This article aims to provoke a discussion around conceiving community members as community-based research facilitators and leaders of their own process of change. It argues this is possible by rescuing Gramsci’s legacy of organic intellectuals that is present in community-based research literature, particularly under the participatory research rubric. However, this perspective has been overshadowed by a strong emphasis on community-based research (CBR) as a collaborative research approach rather than a people’s approach for knowledge production that leads to social transformation. Furthermore, such a view of community-based research is fruitful within an adult education and social movement learning framework. In a sense, social movements provide an environment that facilitates critical consciousness and the formation of organic intellectuals and in which communities and academics learn to better engage in partnership for community-led social change. In this context, CBR is still a collaborative approach, but one led primarily by organic intellectuals.

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)0.960
metaresearch head score (Gemma)0.745
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 score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9600.745
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.8470.001
Scholarly communication0.0030.001
Open science0.0040.004
Research integrity0.0000.860
Insufficient payload (model declined to judge)0.0010.000

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 head0.380
GPT teacher head0.444
Teacher spread0.064 · 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
Published2022
Admission routes2
Has abstractyes

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