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Eliciting Big Data From Small, Young, or Non-standard Languages: 10 Experimental Challenges.

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Summary

Fieldwork in linguistic communities with small, young, or non-standard languages presents unique challenges. This study identifies and offers solutions for data collection, participant recruitment, and experimental design issues in such settings.

Keywords:
big datadialectexperimental designfieldworkrich datasign language

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Area of Science:

  • Linguistics
  • Sociolinguistics
  • Fieldwork Methodology

Background:

  • Researching small, young, or non-standard languages poses distinct fieldwork challenges.
  • Eliciting rich data requires addressing issues related to language characteristics and community dynamics.

Purpose of the Study:

  • To identify and analyze challenges in linguistic fieldwork for under-resourced languages.
  • To provide recommendations for effective data collection, participant recruitment, and experimental design.

Main Methods:

  • Analysis of static and dynamic linguistic characteristics impacting fieldwork.
  • Examination of interactive relations between language features and research processes.
  • Case examples from diverse linguistic communities.

Main Results:

  • Challenges include lack of orthography, rapid language change, absence of standardized tools, and limited participant pools.
  • Issues span data handling, recruitment, experimental design, funding, and cross-border data transfer.
  • Ten experimental challenges are detailed, applicable across spoken, sign, extant, and extinct languages.

Conclusions:

  • Effective fieldwork in under-resourced languages requires strategic planning to overcome inherent linguistic and logistical hurdles.
  • Recommendations are provided to enhance data quality and research feasibility in diverse linguistic contexts.