To Boldly Go Beyond Downloads: How Are Journal Articles Shared and Used?
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.
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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 | 0.026 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.038 | 0.053 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".