Сравнение на методи
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| Събиране на документи от разстояние× | Събиране на данни чрез API× | |
|---|---|---|
| Област | Методология на проучванията | Методология на проучванията |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2000s–present (digital shift of traditional document collection) | 2000s–2010s (formalized as a research method) |
| Създател≠ | Rooted in classical document analysis (Bowen 2009; Scott 1990); remote modality formalized in digital humanities and qualitative online research from the 2000s onward | Emerged from computational social science and web 2.0 platform practices |
| Тип≠ | Qualitative / mixed-methods data collection technique | Digital data collection technique |
| Основополагащ източник≠ | Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Други названия | digital document retrieval, online archival collection, virtual document gathering, remote archival research | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Свързани | 5 | 5 |
| Резюме≠ | Remote Document Collection is a data collection technique in which researchers gather written, visual, or multimedia documents from digital sources — online archives, institutional repositories, cloud storage, email, or government databases — without requiring physical presence. It extends classical document analysis into digital environments, enabling access to geographically dispersed or restricted materials and making it especially valuable for large-scale, cross-national, or time-sensitive research projects. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
| ScholarGateНабор от данни ↗ |
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