Categorizing Blogs as Information Sources for Libraries and Information Science
Bibliographic record
Abstract
Blogs are important sources of information currently used in the work of professionals, institutions and academics. Nevertheless, traditional information needs and uses research has not yet discussed where blogs fit in the existing typologies of information sources. Blogs and other types of social media have several characteristics that blur the lines of distinction existent between traditional information source categories. This chapter brings this research problem to the fore. Not only do we examine why blogs do not neatly fit into existing information source categories, but we also deliberate the implications for libraries in terms of the need to consider blogs as an information source to be included in collection development. We discuss the opportunities and possibilities for blogs to be integrated into the collection development efforts of academic and public libraries to better serve patrons. In order to accommodate for blogs and other types of social media as information sources, we propose the introduction of an additional information source category. We suggest new avenues of future research that investigate how blogs are being used to meet information needs in various social settings, such as corporations, health care and educational settings (e.g., higher education, and schools). In this chapter, we develop a framework of how blogs may function as information sources to provide libraries with a better understanding of how blogs are integrated into the context of everyday information seeking. By grouping the ways in which people employ blogs to acquire information, we propose that blogs provide information sources along a continuum ranging from non-fiction to fictional information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".