Bibliographic record
Abstract
As we look back on Good Friday and Easter our memories are often focused upon the grimness of the picture. We behold our Lord bearing his cross through the streets lined with the people who had hailed him as the Messiah only a week before. We witness his silent affliction at the hands of rude and rough soldiers, who strip him and affix him to the cross. Here are broken flesh, flowing blood, the jarring crash ofthe hammer, loud cries and tears. We hear his terrible cry, "my God, my God, why have you forsaken me?" Here is a picture of the deepest of human agony and absolutely innocent suffering. The suffering of the innocent is the darkest of mysteries. Human pain and anguish would make some sense if it were only the most evil among us who suffered. But that's not the way it is. The pain of disease, the sorrow of early death and of loneliness, the hardships of poverty and unemployment, fall upon all kinds of people regardless of how good or bad they are. The suffering of the innocent is sometimes bad enough to shake our faith in God. Sometimes it makes people hate God with righteous indignation. Jesus' experience offorsakenness is a common one for humanity. It is not a matter of physical suffering only but is accompanied by mental anguish, the fright and terror of doubt, doubt of God's goodness and love, which is the very ultimate in human misery and despair. Like Job of the Old Testament, many of us may yet cry out before we die, "better that I had never been born! Better dead at birth! Why is death withheld from me now?" .
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".