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Record W2782226670 · doi:10.26522/brocked.v27i1.626

Moving from Chasm to Convergence: Benefits and Barriers to Academic Activism for Social Justice and Equity

2017· article· en· W2782226670 on OpenAlexvenueno aff
Barbara Rose

Bibliographic record

VenueBrock Education Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyEquity (law)Social justiceLiminalitySocial activismNarrativeEducational equityHigher educationPublic relationsPedagogyPolitical scienceSocial scienceLawPolitics

Abstract

fetched live from OpenAlex

There are many natural links between academic work and activism that can be used for social justice and equity, but remain underdeveloped in higher education. Using the concept of liminality and the inclusion of personal voice that is central in Scholarly Personal Narrative methodology, this article explores academic activism in multiple ways. First, a series of “purposeful conversations” with educators at the University of Malta suggest that the level of self-affiliation with activism is influenced by academic discipline and the presence of impactful successes related to activism. Challenges within academic activism include devaluing activism within academic structures, and balancing the roles and actions of academic, activism, and personal lives. Second, benefits of activism in student learning are described, including (a) using synoptic learning as a curricular organizing concept for intellectual development, equity, and social justice and (b) exploring activism as a robust organizing concept for student learning across disciplines. Third, systemic barriers (e.g., maintenance of privilege) and anti-activism as moral high ground (e.g., activism as dangerous, too radical, narrowly appropriate, and unnecessary) are identified using two examples of curricular approval processes at a Midwestern university. Fourth, strategies to disrupt barriers to academic activism suggested by these results are presented.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.504
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.437
Teacher spread0.337 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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