Librarians Are Attracted to Blogs That Support Professional Continuing Education
Bibliographic record
Abstract
Objective – The purpose of the study was to examine how librarian blogs are being used for communication within the profession. Design – The method used was content analysis and unstructured interviews. Setting - The researcher is based out of a state university in the United States of America. Subjects - Content of and communication within 12 librarian blogs were analyzed. Seven of the 12 bloggers were interviewed. Methodology – There were 15 blogs identified in a list by Quinn (2009) and reduced to the 12 best suited for the study. Over a 24-month period (January 2009-December 2010), random samples of posts with 2 or more comments were selected for each month from the 12 blogs and analyzed. All comments related to these selected posts were also analyzed. The researcher categorized the blogs overall, plus individual posts, into one of four predominant genres (social, professional development, political, and research). Content was coded based on previous coding methodology for blog content found in the research literature. Requests for interviews were sent to all 12 bloggers with 7 agreeing to be interviewed. Preliminary results of the content analysis for his/her own blog were shared with each blogger before the interview took place. Inter-coder reliability was pretested and found to be 83.33%. Main Results - Two hundred eighty-eight posts randomly chosen received 1936 reader comments. Bloggers responded to these comments 254 times. Blogs were categorized under the “social” genre most frequently (53%), followed by “professional development” (31%), “political” (14%), and “research” (2%; percentages were rounded to the nearest whole number by the reviewer). Professional development was the lead genre in two of the individual blogs. All seven bloggers interviewed stated that professional development is a large focus of their blogs. Reasons for blogging ranged from the importance of sharing information, contextualizing information, and (for some) satisfying personal ambition. There was a common personal enjoyment of writing and all planned to continue blogging despite increasing time constraints. Conclusion - Professional development is a major focus of content in librarian blogs. Blog posts and comments stay on topic throughout exchanges between bloggers and readers.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".