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Record W2913783096

Spanish for Law Enforcement Enhanced Edition: The Basic Spanish Series - iLrnTM Heinle Learning Center, 4 terms (24 months) Printed Access Card

2016· book· en· W2913783096 on OpenAlexaboutno aff
Ana C. Jarvis, Raquel Lebredo

Bibliographic record

VenueCengage Learning eBooks · 2016
Typebook
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLaw enforcementVocabularyVariety (cybernetics)EnforcementPerspective (graphical)Center (category theory)Quarter (Canadian coin)SociologyLawPublic relationsLinguisticsPolitical sciencePsychologyComputer scienceArtificial intelligenceHistory
DOInot available

Abstract

fetched live from OpenAlex

BASIC SPANISH FOR LAW ENFORCEMENT is a career manual designed to serve those in the law enforcement professions who seek basic conversational skills in Spanish. Written for use in two-semester or three-quarter courses, it presents typical everyday situations that pre-professionals and professionals may encounter when dealing with Spanish speakers in the United States at work settings such as police stations, prisons, and on the street. This second edition introduces practical vocabulary, everyday on-the-job situations, and culture notes (Notas culturales) written from a cross-cultural perspective. It provides opportunities to apply, in a wide variety of practical contexts, the grammatical structures presented in the corresponding lessons of the Basic Spanish core text.

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)0.000
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 score0.276
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2760.180

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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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
Published2016
Admission routes1
Has abstractyes

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