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Article published In: Linguistic Landscape
Vol. 10:3 (2024) ► pp.302–334

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Modeling Linguistic Landscapes

A comparison of St Martin’s two capitals Philipsburg and Marigot

ORCiD logo with link | TU Dortmund University
 | TU Dortmund University
 | TU Dortmund University
Published online: 3 September 2024
DOI logo with link
https://doi.org/10.1075/ll.23070.bus
Abstract
Much work in Linguistic Landscapes approaches multilingual landscapes in qualitative terms; inferential statistical approaches are still underrepresented. The present paper adds to filling this methodological gap by investigating the Linguistic Landscapes of St Martin, a highly multilingual, eastern Caribbean island, divided into a formerly Dutch-colonized and French-colonized part. It does so by employing an inferential statistical method to study the nature of linguistic diversity on signs. Based on 372 and 373 signs respectively for each of the commercial districts of Philipsburg and Marigot, we quantitatively analyze the occurrences of languages on the different signs and statistically model the data by means of Multi-Label Classification (MLC; e.g. Rivolli, A. & de Carvalho, A. (2018). The utiml Package: Multi-label Classification in R. The R Journal, 10(2), 24–37. Google Scholar logo with link to Google Scholar). The results show that both shopping districts are characterized by multilingual patterns of Linguistic Landscapes but with differences in their exact linguistic setups, most importantly the interplay and relationships between different languages, motivated by differences in their sociolinguistic setup.
Abstrakt
Die Erforschung von Sprachlandschaften hat vor allem in den letzten beiden Jahrzehnten erheblich an Interesse und Bedeutung gewonnen. Neuere Arbeiten befassen sich insbesondere mit qualitativen Forschungsansätzen zu mehrsprachigen linguistischen Landschaften; inferenzstatistische Ansätze sind weitestgehend unterrepräsentiert. Die vorliegende Studie trägt dazu bei, diese methodische Lücke zu schließen, indem sie Sprachlandschaften der mehrsprachigen Karibik-Insel St. Martin untersucht, die in einen ehemals niederländisch und einen französisch kolonisierten Teil geteilt ist. In dieser Studie wird eine inferenzstatistische Methode angewandt, um die visuelle sprachliche Vielfalt St. Maartens zu untersuchen und die beiden Teile zu vergleichen. Es werden drei miteinander zusammenhängende Forschungsfragen untersucht: Erstens, welche Sprachen dominieren die Beschilderung in der Sprachlandschaft der wichtigsten Einkaufsbezirke in den beiden Hauptstädten St. Martins? Zweitens, welche Faktoren beeinflussen die Sprachwahl auf mehrsprachigen Schildern in den Haupteinkaufsbezirken St. Martins? Und nicht zuletzt, welche Ähnlichkeiten und Unterschiede gibt es bezüglich der mehrsprachigen Beschilderung in den Einkaufsvierteln im niederländischen und französischen Teil der Insel? Auf der Grundlage von 372 bzw. 373 Schildern für jedes der Einkaufsviertel von Philipsburg und Marigot analysieren wir quantitativ das Vorkommen von Sprachen auf den verschiedenen Schildern und modellieren die Daten statistisch mit Hilfe der Multi-Label-Klassifikation (MLC; z. B. Rivolli, A. & de Carvalho, A. (2018). The utiml Package: Multi-label Classification in R. The R Journal, 10(2), 24–37. Google Scholar logo with link to Google Scholar).
Die Ergebnisse zeigen, dass beide Einkaufsbezirke mehrsprachige Muster aufweisen, sich diese jedoch in ihrer genauen sprachlichen Ausgestaltung unterscheiden, vor allem was das die Interaktion zwischen den verschiedenen Sprachen betrifft. Diese sind vornehmlich durch Unterschieden im soziolinguistischen Kontext der beiden Teile der Insel motiviert.
Article outline
  • 1.Introduction
  • 2.The framework of the Linguistic Landscape
    • 2.1Language distribution on signs
    • 2.2Quantitative approaches to LL
    • 2.3Research on Linguistic Landscapes in multilingual island communities
    • 2.4The case of St Martin
  • 3.Investigating Linguistic Landscapes of St Martin: Data and method
    • 3.1Location
    • 3.2Method of data collection, coding, and analysis
    • 3.3Statistical analysis
    • 3.4Classification models
  • 4.Results
  • 5.Discussion and conclusions
  • Acknowledgements
  • Notes
  • References
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