Bibliographic record
Abstract
In this morning's gospel lesson, Jesus tells the story of the good Samaritan, one of the most beloved of stories in the Bible and one unique to the gospel of Luke.It's important for us to note, however, that this parable is a part of an exchange with a lawyer, a lawyer who sets out to test Jesus: "What must I do to inherit eternal life?"And Jesus replies, "What is written in the law, what do you read there?"But here, we need to stop for a moment, because Jesus doesn't really say "What is written in the law, what do you read there?"That is what we read in our bibles and we read that because what is written in Greek, the language of the New Testament is something a bit more curious, a bit more cryptic, a little less clear than the translation but a lot more intriguing.Let me give you a more literal translation of Jesus' response to the lawyer who questions: "What must I do to inherit eternal life?"In true Talmudic fashion Jesus answers this question with a question -actually with two questions: First, "In the law, what is written?"And then, "How do you read?" 2 In the law, what is written?How do you read?How do you read?Not what do you read, but how do you read?Now this is a curious question, a questionable question, in fact.What kind of a response would such a question elicit?I read well?I read slowly?I read often?I rarely read?How do you read?The lawyer goes on to answer Jesus' two questions by first answering the first question about what is written in the law.He says: "You shall love the Lord your God with all your heart, and with all your soul, and with all your strength, and with all your mind and your neighbour as yourself."The lawyer tells Jesus and us what is written in the law, but in so doing he also tells us how he reads because his answer is revealing.His answer tells us something about his reading strategy, In answering the first question the lawyer quotes two passages: one from Deuteronomy and one from Leviticus; and in quoting the Deuteronomy passage the lawyer expands or adds on to what we
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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".