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Record W2289528059

Thinking Like a Lawyer

2008· article· en· W2289528059 on OpenAlexaff
Dan Priel

Bibliographic record

VenueSSRN Electronic Journal · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsYork University
Fundersnot available
KeywordsAnalogyAnalogical reasoningSkepticismCorrectnessEpistemologyDeductive reasoningNothingPractical reasonComputer scienceLawPhilosophyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Many legal theorists have argued that analogical reasoning is merely rule-following in which the general rule is not stated. Lloyd Weinreb's tries to defend the practice of analogical reasoning on its own terms. He does so by giving examples of the way people use analogical reasoning, both in legal and non-legal contexts, as a means for deciding how to act in particular circumstances. By itself such evidence does not support Weinreb's case, because to justify analogy he must show that analogical reasoning can somehow lead us to correct answers. Moreover, his evidence does nothing to challenge the claim that analogical reasoning is simply following suppressed rules.I try to give Weinreb a helping hand. I use the data he mentions that people actually reason by analogy as a starting point for a novel justificatory account of analogical reasoning based on the notion of reliable results: a process of reasoning is reliable, and therefore reliance on it justified, if it tends to generate correct results. This requires explaining what counts as correct results in law, which is a question of political philosophy. I show that the most fundamental condition for the acceptability of analogical reasoning is that the standard of legal correctness is not determined by principles derived by Reason (as is the approach in the civil law tradition) but rather by a standard of acceptance (more prevalent in the common law tradition). Nonetheless, I argue that there two other conditions - shared social values and skepticism about the possibility of discovering the right normative foundations to legal questions - that together have to obtain in order to justify reliance on analogical reasoning. Since that in a modern society it is unlikely that these two conditions are not likely to obtain, I conclude that even this sympathetic reconstruction of Weinreb's argument ultimately fails.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.230
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

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