MétaCan
Menu
Back to cohort
Record W4392266173 · doi:10.51644/acvb8501

The Good Samaritan

2008· article· en· W4392266173 on OpenAlexaff
Allen Jorgenson

Bibliographic record

VenueConsensus · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsRegional Municipality of Waterloo
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0690.016

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.

Opus teacher head0.038
GPT teacher head0.296
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueConsensusSame topicReligion, Society, and DevelopmentFrench-language works237,207