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Record W7005870147

Service to the Nation: A Living Legal Value for\nJustice Lawyers in Canada

2009· article· en· W7005870147 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeLegal ethicsVirtueValue (mathematics)Virtue ethicsGovernment (linguistics)Meta-ethicsNursing ethicsInformation ethics
DOInot available

Abstract

fetched live from OpenAlex

Lawyers working within a living government require a living ethics, an approach to ethics that accounts for their day-to-day professional lives within the Department of Justice Canada. There are different archetypes of Justice lawyers, and thus a living ethics is also an ethics of place, one which is sensitive to the government institutions within and for which lawyers work and the functions they accomplish. The focus of this paper, which employs a virtue ethics methodology, is primarily civil litigators. Distinguishing between values (enduring beliefs that influence action) and ethics (the application of values in practice), the paper proposes "service to the nation" as a value that all Justice lawyers share, and describes how that value grows into an ethics that is specific to Justice civil litigators. Service to the nation comprises in its very fabric a conception ofpublic service which, in turn, requires an investigation of who Justice lawyers' clients are and how the lawyers' public interest mandate informs their professional lives. In the language of virtue ethics, "service to the nation" is the "characteristic function" common to all Justice lawyers. Virtue ethics teaches that the ethical practice ofa lawyer can be facilitated through developing professional mentorship relationships with virtuous people. Both rules and roles provide ethical direction, but it is role models, not rules in professional codes of conduct, that are the focal point of ethical deliberation.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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 designNot applicable
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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueeYLS (Yale Law School)Same topicBiological and pharmacological studies of plantsFrench-language works237,207