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Enregistrement W2016224432 · doi:10.1353/lan.2007.0109

<b>Language matters:</b> A guide to everyday questions about language. By Donna Jo Napoli. Oxford: Oxford University Press, 2003. Pp. x, 198. ISBN 0195160487. $19.95.

2007· article· en· W2016224432 sur OpenAlexaboutno aff
Gregory Ward

Notice bibliographique

RevueLanguage · 2007
Typearticle
Langueen
DomainePsychology
ThématiqueLanguage, Metaphor, and Cognition
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)LinguisticsLanguage acquisitionComprehension approachOn LanguageEveryday lifeScope (computer science)PsychologyComputer scienceCognitive scienceLanguage educationEpistemologyHistoryPhilosophy

Résumé

récupéré en direct d'OpenAlex

Reviewed by: Language matters: A guide to everyday questions about language by Donna Jo Napoli Gregory Ward Language matters: A guide to everyday questions about language. By Donna Jo Napoli. Oxford: Oxford University Press, 2003. Pp. x, 198. ISBN 0195160487. $19.95. In a nutshell, this is a great little book—small in size (measuring 5″ × 7″) and short in length (198 pp.), but grand in scope. Billed as ‘A guide to everyday questions about language’, this book provides answers to what are arguably the dozen most common questions that professional [End Page 654] linguists are asked by nonspecialists (including students and highly educated nonlinguists as well as nonacademics). According to the author, each of the answers to these twelve questions is designed to address one or more misconceptions associated with that question, and each question constitutes a separate chapter, ranging in length from eleven to twenty-two pages. Part 1 (Chs. 1–6) deals with language as a human ability and Part 2 (Chs. 7–12) deals with language in its broader social context. Ch. 1 (‘How do we acquire language?’) provides a very general overview of the basic features of language acquisition, with an emphasis on its biological basis: ‘For the past half century, linguists have hypothesized that there is a language mechanism in the brain, an actual physical mechanism, that is responsible for all aspects of language, including learning, processing, and production’ (5). Of course the validity of this claim depends in part on what is meant by ‘all aspects of language’; presumably some of the more context-sensitive aspects of language (i.e. those governing conversational and social interaction or particularized conversational implicature) are not entirely governed by the same mechanism that controls syntax and phonology. That notwithstanding, the chapter gives the reader a very accessible introduction to one of the major findings of the discipline that also serves as a source of rampant misconceptions about language, namely: ‘We are hard-wired to process and produce natural human language’ (15). Ch. 2 (‘From one language to the next: Why is it hard to learn a second language? Why is translation so difficult?’) introduces the reader to some of the issues and problems associated with translation. N uses a Dutch nursery rhyme as an illustration. For example, should proper names (and diminutives) be imported directly into the target language or should social/stylistic equivalents be found? Should the preservation of rhyme and meter be at the expense of the preservation of lexical meaning? Should cultural equivalence be preserved? N uses the example of turkey being mentioned in the context of a festive dinner table, connoting perhaps an American thanksgiving dinner. In translating ‘turkey’ into other languages, should a different, culturally appropriate, food item be used to evoke the same imagery? Or should the translation be as ‘literal’ as possible (whatever that means)? The result of this line of questioning is the inevitable conclusion that ‘translation is not a mechanical act; it doesn’t proceed by any sort of simple algorithm. Rather, translation is a creative act’ (33). Ch. 3 (‘Does language equal thought?’) addresses the interrelated questions ‘Do we think in language?’ and ‘Could we think without a language?’. No introductory discussion of language and thought would be complete, of course, without reference to that old linguistic chestnut regarding the number of words for snow in Inuit, and N does not disappoint (although the Sapir-Whorf hypothesis is never mentioned by name). As N puts it, ‘There are many things we are surrounded by in great quantity that we do not have lots of words for’ (42). As a further illustration of the independence of language and thought, N uses the nonexistence of resultative secondary predicates (e.g. John beat the eggs stiff) in Romance languages to illustrate that the difference between English and Romance in this regard is not conceptual but grammatical. While a fine example of the general point, the use of linguistic jargon here might be a little off-putting to some readers. Ch. 4 (‘Are sign languages real languages?’) covers some basic linguistic facts and figures (the arbitrariness of the sign, the creative nature of language), while embedding them in a novel context...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,168
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,283
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2007
Routes d'admission1
Résumé présentoui

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