Awareness of Altmetrics among LIS Scholars and Faculty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.032 | 0.064 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.060 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it