Guest Editors' Introduction II: Reflections on Scholarship and Activism In Canada and Ireland
Bibliographic record
Abstract
This second of two back-to-back special issues for Studies in Social Justice (SSJ) features articles that offer a spectrum of case studies addressing the relationship between scholarship and activism in Canada and Ireland. As the guest editors of these two issues, we requested that the authors, who represent a range of disciplines and positions in their respective institutions of higher education, employ a critical and self-reflexive lens to examine the challenges and rewards associated with negotiating their roles as scholars and activists. In collaboration with myriad organizations and networks, these authors strive for greater social justice within their local communities. Their efforts are, however, hampered by the unrelenting encroachment of neoliberalism within and beyond the ‘ivory tower.’ Consequently, many focus their discussion on the rising levels of managerialism and corporatization within higher education, highlighting the metrics-oriented response to austerity measures that forefronts monetizable ‘deliverables’ and, in the process, negatively impacts the ability of academics to engage in activist endeavours (see in this issue Brulé; Giroux, Karmis & Rouillard; Hawthorne-Steele, Moreland & Rooney; Luka, Harvey, Hogan, Shepherd & Zeffiro; Murphy; and Visser; see also, e.g., Mercille & Murphy, 2015; Walsh & Loxley, 2014; Washburn, 2005).
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".