Bibliographic record
Abstract
To this point arguments in this text have been based largely on philosophical and psychological grounds. Chapter Two offered primarily psychological views of kindness and empathy development, and Chapter Three outlined views of psychologists such as Prager and Erikson along with educators and philosophers such as Carla Rinaldi to support the arguments made with respect to intimacy and bonding. While those disciplines remain of consequence, sacrifice and forgiveness have special relevance to religion. The term sacrifice is derived from the Latin word sacrificium that means ‘to make sacred’. The religious connotations from which the modernday word is derived are interesting given the continued emphasis present notions of sacrifice receive in religious discourse, particularly (but obviously not limited to) Christian religious discourse. Current notions of the meaning of the word sacrifice tend to include ideas of giving up something, going without something, or making some kind of special effort that is ultimately to the detriment of self but to the advantage of others (Gelven, 1988). According to Noller (1996) sacrifice is an essential element in a definition of true love, sitting alongside caring, respect, and loyalty. The most obvious example of sacrifice in religion is the Christian notion of Christ’s sacrifice on the cross. The idea that engaging in such behaviour is noble, worthwhile, righteous, and even holy has come down to those of us in the western world through the centuries as a result of the Judeo-Christian tradition, and those who have grown in the western tradition are impacted by it whether an individual considers him or herself Christian or not. It is a part of the shared western cultural heritage. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".