Bibliographic record
Abstract
Sacred Snaps tells the story of a new approach to interfaith engagement. It is an invitation to see and engage religion, diversity, and inclusion through the lens of the mobile phone camera. These days, just about everyone owns a camera equipped cell phone. What if we recruited these cameras for the common good? When religion shows up in everyday life—at work, school, the mall, or the beach—often it is not welcome. At a time when so much of the public discourse is around equity, diversity, and inclusion, religion seems peripheral to the conversation. Many embrace the wisdom that our workplaces, schools, and communities are enhanced when people can bring their whole selves into every aspect of their daily lives. But religion and spirituality are not gaining the same ground as other aspects of diversity such as race, ethnicity, gender, sexuality, and ability. To be more fully included in the cultural conversation about human flourishing, religion needs to be seen and heard in new ways. The old paradigm of interreligious dialogue is no longer adequate. A new paradigm focused on building relationships at the grass roots of daily life is emerging. This cutting-edge volume brings together Christians and Muslims in the United States and Canada to explore what their beliefs, practices, and values look like in everyday life.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".