Bibliographic record
Abstract
In 2016, Aaron Rosen co-founded Stations of the Cross , an international, multisite public arts project whose mission is “to use the story of the Passion to prompt reflection and action in response to challenges of social justice.” The exhibition draws upon the devotional practice many Christians follow during Lent, in which they retrace the final fourteen episodes (stations) of Jesus’ journey through Jerusalem, from condemnation to crucifixion and entombment. In each city that hosts the project, curators design a bespoke, fourteen-stop route, marked by both existing and specially commissioned works of art. To date, the exhibition has been staged in London, Washington, DC, New York City, Amsterdam, Deventer, and Toronto. In this essay, Rosen considers how an exhibition explicitly inspired by Christian history and practice can enrich Christian observance while simultaneously providing avenues for deep but non-confessional engagement by non-Christians. The chapter focuses on two modes of religious and aesthetic engagement through whichStations fosters encounters within and between faiths: embodied looking and attentive walking. Conceiving of an exhibition qua pilgrimage, and vice versa , the chapter suggests, reveals important strategies for interfaith dialogue.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".