MétaCan
Menu
Back to cohort
Record W2338188734 · doi:10.26522/ssj.v9i2.1152

Transforming Communities through Academic Activism: An Emancipatory, Praxis-led Approach

2016· article· en· W2338188734 on OpenAlexvenueno aff
Isobel Hawthorne-Steele, Rosemary Moreland, Eilish Rooney

Bibliographic record

VenueStudies in Social Justice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsnot available
Fundersnot available
KeywordsPraxisTransformative learningSociologyGeneral partnershipSocial activismDisadvantageSocial transformationPublic relationsSocial changePolitical sciencePedagogyLawPolitics

Abstract

fetched live from OpenAlex

This article tracks the engagement of university faculty in academic and community activism during thirty years in conflict-affected Northern Ireland. Over time, the team of three academics who wrote the article developed programs to help tackle educational disadvantage in a deeply divided society riven with violent conflict. Our pedagogical approach was driven by social justice principles in practice. In the process, students became what Ledwith & Springett (2010) describe as participative activists in the academy and in their own communities. The aim of this collective activism was to foster transformative change in a society that is now in transition from conflict. Key examples of critical practice are described. We use a case study approach to describe challenges faced by faculty and participants. We argue that academic activism and community partnership can play a positive role in community transformation in the most difficult circumstances.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0180.036
Scholarly communication0.0200.008
Open science0.0040.025
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.156
GPT teacher head0.454
Teacher spread0.298 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueStudies in Social JusticeSame topicAdult and Continuing Education TopicsFrench-language works237,207