Introduction to special section: Inequitable Ruptures, Rupturing Inequity: Theorizing COVID-19 and racial injustice impacts on International Service Learning pedagogy, frameworks and policies
Bibliographic record
Abstract
This introduction to the special section, argues that this pandemic time has been one of ruptures which unveiled ongoing and intersecting social pandemics such as anti-Black and anti-Indigenous racism, white supremacy, patriarchy, classism, and ableism in the context of the COVID-19 global health pandemic (Brand, 2020). We proposed three ruptures as moments for imagining - and doing – otherwise: (i) the Black Lives Matter movement and increased mainstream attention to racial inequity, (ii) COVID-19 and new imaginings of travel, mobility, and safety (iii) mutual aid as increasingly necessary in a pandemic and as a possible relational way forward. We take these ruptures as a starting point for re-imagining learning and movement as relational. This introduction takes up a contextualization and conceptualization of the field of GSL, an overview of the critical literature in this space, and introduces the two articles in the special section. It ends with the hope that the grapplings and reckonings in this section will help scholars and practitioners think through this present moment and re-orient GSL in more just and equitable ways.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".