Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Abstract Generally speaking, ‘focus’ refers to the portion of an utterance which is especially informative or important within the context, and which is marked as such via some linguistic means. It can be difficult to provide a single precise definition, as the term is used somewhat differently for different languages and in different research traditions. Most often, it refers to the linguistic marking of (i) contrast, (ii) question-answering status, (iii) exhaustivity, or (iv) discourse unexpectability. An illustration of each of these possibilities is given below. In English, the focus-marked elements (indicated below with brackets) are realized with additional prosodic prominence in the form of a strong pitch accent (indicated by capital letters). (i) An [AMERICAN] farmer met a [CANADIAN] farmer…(ii) Q: Who called last night?A: [BILL] called last night.(iii) Only [an ELEPHANT] could have made those tracks.(iv) I can’t believe it: The Ohioans are fighting [OHIOANS] ! The underlying intuition common to all these instantiations is that a focus represents the minimal information needed to convey an important semantic distinction. Focus can be signaled prosodically (e.g., in the form of a strong pitch accent), syntactically (e.g., by moving focused phrases to a special position in the sentence), or morphologically (e.g., by appending a special affix to focused elements), with different crosslinguistic focus marking strategies often carrying slightly different restrictions on their use. Example (i) evokes a set of two contrasting alternatives, {‘American farmer,’ ‘Canadian farmer’}, and the meaning ‘farmer’ is common to both members of the set. That is, within this evoked set of alternatives, ‘farmer’ is redundant, and it is the nationality of the farmers which differentiates the two people. Example (ii) exhibits a similar property. One of the standard theories of question semantics represents questions as sets of possible appropriate answers. For (ii), this would be a set of propositions like {‘Bill called last night, ‘Sue called last night,’ etc.}. As with (i), there is an evoked set of meanings whose members share some overlapping semantic material. Within this set, the verb phrase meaning ‘called last night’ is redundant, and it is the identity of the subject that serves to differentiate the true answer. Example (iii) demonstrates a relationship between focus and certain words like only. The sentence means something like ‘of all the animals who might have made these tracks, it must be an elephant.’ As with (i) and (ii), this involves a set of alternatives: the set of possible track makers. That the sentence serves to single out a unique member of this set as being the true track maker makes the subject an elephant a natural focus of the sentence. Finally, in (iv), we see that focus on ‘Ohioans’ is being used to contrast the semantic content of the sentence with some preconception, namely that Ohioans are unlikely fighters of Ohioans. Examples (iii) and (iv) point to more specific uses of focus in different languages. In Hungarian, so-called identificational focus, which is marked syntactically, requires an exhaustive interpretation, as if a silent only were present. And in some Chadic languages, a meaning of “discourse unexpectability,” as in (iv), is required to mark focus via syntactic or morphological means.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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