Moving from Chasm to Convergence: Benefits and Barriers to Academic Activism for Social Justice and Equity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".