{"title":"对夏威夷夏威夷小船渔业调查的自动内容分析揭示了微妙的、不断演变的冲突","authors":"A. Suan, Kirsten M. Leong, K. Oleson","doi":"10.5751/ES-12708-260409","DOIUrl":null,"url":null,"abstract":"Manual content analysis provides a systematic and reliable method to analyze patterns within a narrative text, but for larger datasets, where human coding is not feasible, automated content analysis methods present enticing and time-efficient solutions to classifying patterns of text automatically. However, the massive dataset needed and complexity of analyzing these large datasets have hindered their use in fishery science. Fishery scientists typically deal with intermediately sized datasets that are not large enough to warrant the complexity of sophisticated automated techniques, but that are also not small enough to cost-effectively analyze by hand. For these cases, a dictionary-based automated content analysis technique can potentially simplify the automation process without losing contextual sensitivity. Here, we built and tested a fisheries-specific data dictionary to conduct an automated content analysis of open-ended responses in a survey of the Hawaiʻi small boat fishery to examine the nature of the fishery conflict. In this paper we describe the overall performance of the methodology, creating and applying the dictionary to fishery data, as well as advantages and limitations of the method. The results indicate that the dictionary approach is capable of quickly and accurately classifying unstructured fisheries data into structured data, and that it was useful in revealing deeply rooted conflicts that are often ambiguous and overlooked in fisheries management. In addition to providing a proof of concept for the approach, the dictionary can be reused on subsequent waves of the survey to continue monitoring the evolution of these conflicts. Further, this approach can be applied within the field of fishery and natural resource conservation science more broadly, offering a valuable addition to the methodological toolbox.","PeriodicalId":51028,"journal":{"name":"Ecology and Society","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated content analysis of the Hawaiʻi small boat fishery survey reveals nuanced, evolving conflicts\",\"authors\":\"A. Suan, Kirsten M. Leong, K. Oleson\",\"doi\":\"10.5751/ES-12708-260409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Manual content analysis provides a systematic and reliable method to analyze patterns within a narrative text, but for larger datasets, where human coding is not feasible, automated content analysis methods present enticing and time-efficient solutions to classifying patterns of text automatically. However, the massive dataset needed and complexity of analyzing these large datasets have hindered their use in fishery science. Fishery scientists typically deal with intermediately sized datasets that are not large enough to warrant the complexity of sophisticated automated techniques, but that are also not small enough to cost-effectively analyze by hand. For these cases, a dictionary-based automated content analysis technique can potentially simplify the automation process without losing contextual sensitivity. Here, we built and tested a fisheries-specific data dictionary to conduct an automated content analysis of open-ended responses in a survey of the Hawaiʻi small boat fishery to examine the nature of the fishery conflict. In this paper we describe the overall performance of the methodology, creating and applying the dictionary to fishery data, as well as advantages and limitations of the method. The results indicate that the dictionary approach is capable of quickly and accurately classifying unstructured fisheries data into structured data, and that it was useful in revealing deeply rooted conflicts that are often ambiguous and overlooked in fisheries management. In addition to providing a proof of concept for the approach, the dictionary can be reused on subsequent waves of the survey to continue monitoring the evolution of these conflicts. Further, this approach can be applied within the field of fishery and natural resource conservation science more broadly, offering a valuable addition to the methodological toolbox.\",\"PeriodicalId\":51028,\"journal\":{\"name\":\"Ecology and Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2021-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecology and Society\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.5751/ES-12708-260409\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecology and Society","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.5751/ES-12708-260409","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Automated content analysis of the Hawaiʻi small boat fishery survey reveals nuanced, evolving conflicts
Manual content analysis provides a systematic and reliable method to analyze patterns within a narrative text, but for larger datasets, where human coding is not feasible, automated content analysis methods present enticing and time-efficient solutions to classifying patterns of text automatically. However, the massive dataset needed and complexity of analyzing these large datasets have hindered their use in fishery science. Fishery scientists typically deal with intermediately sized datasets that are not large enough to warrant the complexity of sophisticated automated techniques, but that are also not small enough to cost-effectively analyze by hand. For these cases, a dictionary-based automated content analysis technique can potentially simplify the automation process without losing contextual sensitivity. Here, we built and tested a fisheries-specific data dictionary to conduct an automated content analysis of open-ended responses in a survey of the Hawaiʻi small boat fishery to examine the nature of the fishery conflict. In this paper we describe the overall performance of the methodology, creating and applying the dictionary to fishery data, as well as advantages and limitations of the method. The results indicate that the dictionary approach is capable of quickly and accurately classifying unstructured fisheries data into structured data, and that it was useful in revealing deeply rooted conflicts that are often ambiguous and overlooked in fisheries management. In addition to providing a proof of concept for the approach, the dictionary can be reused on subsequent waves of the survey to continue monitoring the evolution of these conflicts. Further, this approach can be applied within the field of fishery and natural resource conservation science more broadly, offering a valuable addition to the methodological toolbox.
期刊介绍:
Ecology and Society is an electronic, peer-reviewed, multi-disciplinary journal devoted to the rapid dissemination of current research. Manuscript submission, peer review, and publication are all handled on the Internet. Software developed for the journal automates all clerical steps during peer review, facilitates a double-blind peer review process, and allows authors and editors to follow the progress of peer review on the Internet. As articles are accepted, they are published in an "Issue in Progress." At four month intervals the Issue-in-Progress is declared a New Issue, and subscribers receive the Table of Contents of the issue via email. Our turn-around time (submission to publication) averages around 350 days.
We encourage publication of special features. Special features are comprised of a set of manuscripts that address a single theme, and include an introductory and summary manuscript. The individual contributions are published in regular issues, and the special feature manuscripts are linked through a table of contents and announced on the journal''s main page.
The journal seeks papers that are novel, integrative and written in a way that is accessible to a wide audience that includes an array of disciplines from the natural sciences, social sciences, and the humanities concerned with the relationship between society and the life-supporting ecosystems on which human wellbeing ultimately depends.