Bibliographic record
Abstract
In light of critiques regarding the concept of service, and after highlighting limits of critical servicelearning and “authentic” relationship approaches, this article presents “making space” for marginalized community perspectives as an alternative metaphor for conceptualizing university-community relationships. Drawing upon multiple experiences with American Indian tribal nations, the article identifies deeply intercultural, counterhegemonic, and decolonizing dynamics enacted through making space, and which produce a discomforting reversal of the common analytic focus on community service recipients. Making space also enables university-community alignment, the generation of projects truly based in community interests, and facilitates interactions outside and disruptive of hegemonic power/knowledge regimes and discourses. The field of service-learning, increasingly supported as a common element of higher education, faces a range of questions about its nature, practice, and theory. Central among these is the very notion of service itself, the types of experience it entails, and students’ relationship to those being served. While service-learning takes some inspiration from Deweyian educational theory that emphasizes problematic and disruptive situations as the origin of inquiry (Dewey, 1916; Garrison 1996, p. 16), and originated in first-generation learning experiences that were frequently only semi-structured and “messy” (Stanton, Giles, & Cruz 1999), much current service-learning appears to be highly structured and orderly. As service-learning increasingly refers to volunteer experiences touted as community service by colleges and universities, the nature of service, and the action and relationships involved in service, are worthy of ongoing debate and discussion. This article does not attempt to craft a new definition of service, however, or parse the multitude of definitions that have been introduced and reviewed elsewhere. Rather, through this article I aim to contribute to the theorization of “service” by problematizing common understandings of service and action while promoting the notion of “making space” as one alternative (Regan, 2010). Making space, a concept drawn from reconciliation efforts involving the Canadian government and First Nations (Regan), is consistent with and extends the growing emphasis within the existing service-learning literature on both “critical” service-learning and sustained community-university relationships. My discussion of the making space metaphor and its potential are based in my participant-observation in communitybased collaborations with Indigenous Nations, interviews with university and tribal participants in such
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.058 | 0.027 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".