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Record W6996949540

The Ten Evidence ‘Rules’ That Every Family Law Lawyer Needs to Know

2016· article· en· W6996949540 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsStatement (logic)HearsayFamily lawIronyBit (key)Federal Rules of Evidence
DOInot available

Abstract

fetched live from OpenAlex

I put ‘‘Rules” in quotation marks above, because I once wrote an article entitled ‘‘Are There Any Rules of Evidence in Family Law?” As I said there about the title:\nI still cannot figure out if my title is facetious, or ironic, or a cri de Coeur of a frustrated counsel, or the evangelical call of an evidence professor, or just an accurate statement of the law. It was originally intended to be facetious, to emphasize that there are no ‘‘rules of evidence” in family law, even if we pretend there are. Or, maybe it is more ironic, to remind ourselves that there really are such ‘‘rules”, even if we say there are not. As counsel, on some days, the long ones, the title was my rant in the halls and environs of courtrooms. As an evidence professor, I believe that the evidence ‘‘rules” serve important systemic purposes, but then that is what you would expect. The more family law cases I read, I did begin to fear that my title was simply accurate. Not only accurate, by acceptably so, with no hint of wit or irony left.\nThe gist of the ‘‘Any Rules” article was eventually that there are evidence ‘‘rules”, or more accurately now, evidence ‘‘principles”, that do have real application in family law cases.\nI was asked to boil down all of evidence law to ten, count ‘em, just ten ‘‘rules” that family law lawyers needed to know. This is a bit like picking the ten best Canadian albums of all time. We all know that Joni Mitchell and Neil Young will make the list. Same with evidence ‘‘rules”: ‘‘relevance” will make the list, as will ‘‘hearsay”, but there will be debate about others. Here’s my list:\n(1) Relevance\n(2) Admissibility Procedure\n(3) Opinion: Lay and Expert\n(4) Hearsay and Its Exceptions\n(5) Business Records\n(6) The Rule in Browne v. Dunn\n(7) Impeaching and Supporting Credibility\n(8) Illegally-Obtained Recordings, E-mails, Etc.\n(9) Privilege for Settlement Negotiations\n(10) Privilege for Confidentiality\nIn what follows, I am purposely keeping the paper brief and to the point. Those who want further details can consult my two articles on the subject or the Evidence in Family Law looseleaf or, if you really want that much more, one of the three leading Canadian textbooks on evidence law. I will not cover evidence issues specific to child protection, focussing upon the issues that most frequently arise in the general range of ‘‘private” family law cases.

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)0.024
metaresearch head score (Gemma)0.077
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.027
Scholarly communication0.0150.019
Open science0.0030.004
Research integrity0.0080.024
Insufficient payload (model declined to judge)0.0100.007

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 head0.052
GPT teacher head0.345
Teacher spread0.293 · 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
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

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