To Boldly Go Beyond Downloads: How Are Journal Articles Shared and Used?
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
With more scholarly journals being distributed electronically rather than in print form, we know that researchers download many articles. What is less well known is how journal articles are used after they are initially downloaded. To what extent are they saved, uploaded, tweeted, or otherwise shared? How does this reuse increase their total use and value to research and how does it influence library usage figures? University of Tennessee Chancellor’s Professor Carol Tenopir, Professor Suzie Allard, and Adjunct Professor David Nicholas are leading a team of international researchers on a the project, “Beyond Downloads,” funded by a grant from Elsevier. The project will look at how and why scholarly electronic articles are downloaded, saved, and shared by researchers. Sharing in today’s digital environment may include links posted on social media, like Twitter, and in blogs or via e-mail. Having a realistic estimate of this secondary use will help provide a more accurate picture of the total use of scholarly articles. The speakers will present the objectives of the study, share the approach and avenues of exploration, and report on some preliminary findings. Furthermore, the speakers will discuss how the potential learnings could yield benefits to the library community.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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