Anglo-Scandinavian language contacts and word order change in early English
Notice bibliographique
Résumé
This study investigates one of the most important changes that the English language underwent in the earlier course of its development – the emergence of a rigid SVO word order. Both internal and external factors have been identified in the literature as influential in the change. Among the latter, contacts with the early Scandinavian population have often been mentioned as providing an important early input. These contacts have also been regarded as one of the factors contributing to the erosion of case inflections, a change implicated in the gradual stabilisation of the SVO order. The main objective of the present study is to assess the role of these external pressures in the establishment of the new syntactic conditions in early English. In a more general perspective, this study evaluates the significance of language contacts in promoting changes in morphosyntax. This research also examines the relevance of an influential theoretical model used in the literature to explain the changes at issue, viz. Johannes Schmidt’s wave theory. The material for the present study consists of two parsed corpora representing Old and Middle English: The York-Toronto-Helsinki Corpus of Old English Prose (YCOE) and The Penn Parsed Corpus of Middle English (PPCME2, Second Edition). Together, these databases comprise almost 3 million words and contain texts from different dialects. The frequency of occurrence of the (S)VO word order in particular dialect sectors is measured and compared from various angles relevant to the study. They include distinct clause settings, focusing on nominal or pronominal constituents of NPs in word order sequences, the potential impact of the date and genre of texts in sets, as well as exploring the differences in word order distributions between texts that are translations from foreign originals and those that represent native material. The multifaceted analysis of data aims, among other matters, to evaluate the usefulness of parsed diachronic corpora in tracking large-scale linguistic changes. The results show that (S)VO developed faster in the dialects of the areas affected by the contact, viz. the North and the East Midlands. This feature seems well established especially at the subordinate clause level. Furthermore, the highest normed frequency values for sequences with pronouns and nouns alike were found in the northernmost dialects. Both findings suggest a more external rather than internal motivation for the structural change. Evidence from genetic and archaeological studies, too, speaks in favour of a clearly marked Scandinavian zone, which temporally extended beyond the so-called Viking Era. With a repeated pattern of the most frequently occurring and most regularly distributed (S)VO particularly in the North, the connection between morphological simplification and the emerging new word order is more than likely. The prominence of the North within the dialectal spectrum likewise points to the existence of a focal area, which provided the starting point of linguistic innovations in a way advocated by the wave model. Set against the socio-political reality of medieval England, the results confirm the existence of a north-south divide, with the former constituting an auspicious setting for fostering changes of all kinds, including those occurring in the language.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».