Bibliographic record
Abstract
Abstract Anthropology's approach to answering the question of the role of suffering in our lives is limited to empirical data and at best describes an individual's capability to endure it and make sense of it. Levinas was at odds to find meaning in suffering once it had exceeded certain proportions. Various cultures demonstrate greater and lesser capacities for integrating corporate suffering when it has crossed a significant threshold (e.g. Israeli Holocaust survivors, Canadian‐Cambodian Khmer Rouge survivors, and their descendants). What is the role of ritual and productive suffering in revealing meaning in suffering? Some examples are drawn from the experiences of pilgrims on the Camino de Santiago de Compostella. Drawing from Dr. Eleonore Stump's exploration of second‐person narratives and relationships, a Christian philosophical‐theological response is provided.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.080 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".