We are all institutionalized: Three works to challenge the conceit of a generically ‘academic’ study of religion
Bibliographic record
Abstract
This essay encounters and considers together three very different recent works by scholars of religion, each one with strong Canadian connections: Maureen Matthews, Aaron Hughes and Donald Wiebe. The primary purpose, however, is to illuminate more broadly the importance of institutional dynamics in the formation and operation of the academic study of religion (i.e., not just in Canada). This stands in contrast to a well-established pattern of debating supposedly loftier questions of naming, disciplinary identity, idealized mandates and limits, etc. Furthermore, this essay suggests that scale of investigation matters – with a local, single-institution study revealing more, perhaps, about how we really do our work than either national or transnational efforts. In the end, reading these three books together suggests a tremendous diversity, including dynamic institutional diversity, in academic approaches to religion: scientific and non-scientific (predictably) but also, disciplined or expert and non-expert or academic administrative. Thus, the essay enjoins readers to take seriously a distinction between domains of ‘distributive’ and ‘concentrated’ expertise within the academy (e.g., Religious Studies versus, say, Civil Engineering), as well as the development of patterns of ‘altero-piety’ across the expert/nonexpert divide. In the end, such murky institutional dynamics appear to be shaping and impelling our field from the local institutional level (e.g., at the University of Winnipeg as documented by Matthews) to the transnational institutional level (e.g., in the International Association for the History of Religions as documented by Wiebe). Ultimately, one must conclude that stipulating that Religious Studies entail the academic study of religion is meaningless. ‘The academy’ is no more universal and unique ( sui generis?) than ‘religion’ itself. Rather, academic institutions are diverse and particular; and yet a variety of factors, ranging from deep colonial histories to the current global political economy of postsecondary higher education, all work to conceal the importance of the institutional basis of Religious Studies. Put another way (and pace Jonathan Z Smith): religion certainly is a creation of the scholar’s study – yet, far from imagining this scholar’s study as a place set apart (as it were), we must start imagining it as a historical, social and institutional location. That would take us one small but further step towards the all-important goal of disciplinary ‘reflexivity”.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".