Bibliographic record
Abstract
Here I offer thoughts on what the recent history of the academic study of religion might reveal about its current state, and why we need to continually renew attention to our collective, and ideally, contested, vision for the academy. I frame this reflection in relation to two scholars of religion, Donald Wiebe and Michel Desjardins, who in the late twentieth and early twenty-first centuries shaped the Canadian and American academic conversation about this field of study. I approach their work in this area not as discrete faits accompli, but as examples of iterative self-construction in the history of the field. Attention should continually be paid to what kind of religious studies we do, and what we study when we study religion; part of that account is of the we, specifically of our relationality, in our entitlements and obligations. I am increasingly persuaded that the key nexus of focus for our attention is not (or is no longer) primarily in the question of the humanistic versus the social-scientific study of religion. Thinking through what we do over against what we think we ought to be doing will involve a less oppositional, more relational accounting of and accounting for who we think we are and what we think we owe one another.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".