Awareness of Altmetrics among LIS Scholars and Faculty
Bibliographic record
Abstract
Altmetrics track the attention paid to scholarship via mentions in social media, the press, and other non-traditional venues. For library and information science (LIS) faculty, altmetrics are also a new and important area for research and teaching. We conducted a survey of LIS faculty teaching in US and Canadian graduate LIS programs accredited by the American Library Association in which we asked about their familiarity with and awareness of measures of research impact, including altmetrics. Our results indicate that while most LIS faculty in our sample had some awareness of altmetrics, they reported greater familiarity with traditional measures of research impact such as citation counts and usage statistics. We also confirmed that, among our sample, there was a relationship between years of teaching experience and awareness of altmetrics, as well as among familiarity with altmetrics, familiarity with citation counts, and familiarity with usage statistics. Among the robust, global body of research related to the use of new measures of research impact among scientists and scholars, there are few studies that use survey methods and focus on faculty scholars within a specific discipline. The results of this study contribute new knowledge to the existing body of research on altmetrics and may contribute to the development of LIS graduate curricula devoted to measures of research impact and their application in practice.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".