TY - JOUR
T1 - Fixing Fieldnotes
T2 - Developing and Testing a Digital Tool for the Collection, Processing, and Analysis of Ethnographic Data
AU - Astrupgaard, Sofie Læbo
AU - Lohse, August
AU - Gregersen, Emilie Munch
AU - Holm Salka, Jonathan
AU - Albris, Kristoffer Langkjær
AU - Pedersen, Morten Axel
PY - 2024
Y1 - 2024
N2 - Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomenacompared to other data sources. Their open-ended and flexible character makes them especiallyuseful in explorative research. However, fieldnotes are typically highly unstructured and per-sonalized by individual researchers, which make them harder to use as a method for datacollection in collaborative and mixed methods research. More precisely, the unstructured natureof ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficultto search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult tomeaningfully integrate fieldnotes with other more quantitative data types such as more such assurveys or geospatial data, and 3) Computational Processability—it is hard to process and analyzefieldnotes with computational methods such as topic models and network analysis. To solve thesethree challenges, we present a new digital tool, for the systematic collection, processing, andanalysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinarymixed methods pilot study on attention dynamics at a political festival in Denmark. Through caseexamples from this study, we show how adopting this new digital tool allowed our team toovercome the three aforementioned challenges of fieldnotes, while retaining the flexible andexplorative character of ethnographic research, which is a key strength of ethnographic fieldwork.Keywordsfieldwork, qualitative social science, mixed methods, ethnography, computational ethnography,machine anthropology, social data science, methodological advances, network analysis1University of Copenhagen, DenmarkCorresponding Author:Sofie L. Astrupgaard, Center for Social Data Science (SODAS), DISTRACT Project, University of Copenhagen, ØsterFarimagsgade 5, Building 1, Copenhagen 1353, Denmark.Email: sofi[email protected] paper was co-first authored by Sofie Læbo Astrupgaard and August Lohse.
AB - Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomenacompared to other data sources. Their open-ended and flexible character makes them especiallyuseful in explorative research. However, fieldnotes are typically highly unstructured and per-sonalized by individual researchers, which make them harder to use as a method for datacollection in collaborative and mixed methods research. More precisely, the unstructured natureof ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficultto search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult tomeaningfully integrate fieldnotes with other more quantitative data types such as more such assurveys or geospatial data, and 3) Computational Processability—it is hard to process and analyzefieldnotes with computational methods such as topic models and network analysis. To solve thesethree challenges, we present a new digital tool, for the systematic collection, processing, andanalysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinarymixed methods pilot study on attention dynamics at a political festival in Denmark. Through caseexamples from this study, we show how adopting this new digital tool allowed our team toovercome the three aforementioned challenges of fieldnotes, while retaining the flexible andexplorative character of ethnographic research, which is a key strength of ethnographic fieldwork.Keywordsfieldwork, qualitative social science, mixed methods, ethnography, computational ethnography,machine anthropology, social data science, methodological advances, network analysis1University of Copenhagen, DenmarkCorresponding Author:Sofie L. Astrupgaard, Center for Social Data Science (SODAS), DISTRACT Project, University of Copenhagen, ØsterFarimagsgade 5, Building 1, Copenhagen 1353, Denmark.Email: sofi[email protected] paper was co-first authored by Sofie Læbo Astrupgaard and August Lohse.
U2 - 10.1177/08944393231220488
DO - 10.1177/08944393231220488
M3 - Journal article
VL - 42
SP - 1223
EP - 1243
JO - Social Science Computer Review
JF - Social Science Computer Review
SN - 0894-4393
IS - 5
ER -