Laying the Groundwork for a New Library Service: Scholar-Practitioner & Graduate Student Attitudes Toward Altmetrics and the Curation of Online Profiles
Bibliographic record
Abstract
Objective – In order to inform a library service related to creating and maintaining online scholarly profiles, we sought to assess the knowledge base and needs of our academic communities. Participants were queried about use, issues, and attitudes toward scholarly profile and altmetric tools, as well as the role librarians could play in assisting with the curation of online reputation. Methods – Semi-structured interviews with 18 scholar-practitioners and 5 graduate students from two mid-sized universities. Results – While all participants had Googled themselves, few were strategic about their online scholarly identity. Participants affirmed the perception that altmetrics can be of value in helping to craft a story of the value of their research and its diverse outputs. When participants had prior knowledge of altmetrics tools, it tended to be very narrow and deep, and perhaps field-specific. Participants identified time as the major barrier to use of scholarly profile and altmetrics tools. Conclusions – Librarians are well-placed to assist scholar-practitioners who wish to curate an online profile or use altmetrics tools. Areas of assistance include: personalized support, establishment of goals, orientation to specific tools, orientation to altmetrics and scholarly promotion landscape, preparing users for potential difficulties, discussing copyright implications, Open Access education, and guidance with packaging content for different venues and audiences.
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.
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 | 0.035 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".