Do articles in open access journals have more frequent altmetric activity than articles in subscription-based journals? An investigation of the research output of Finnish universities
Bibliographic record
Abstract
Abstract Scientific articles available in Open Access (OA) have been found to attract more citations and online attention to the extent that it has become common to speak about OA Altmetrics Advantage. This research investigates how the OA Altmetrics Advantage holds for a specific case of research articles, namely the research outputs from universities in Finland. Furthermore, this research examines disciplinary and platform specific differences in that (dis)advantage. The new methodological approaches developed in this research focus on relative visibility, i.e. how often articles in OA journals receive at least one mention on the investigated online platforms, and relative receptivity, i.e. how frequently articles in OA journals gain mentions in comparison to articles in subscription-based journals. The results show significant disciplinary and platform specific differences in the OA advantage, with articles in OA journals within for instance veterinary sciences, social and economic geography and psychology receiving more citations and attention on social media platforms, while the opposite was found for articles in OA journals within medicine and health sciences. The results strongly support field- and platform-specific considerations when assessing the influence of journal OA status on altmetrics. The new methodological approaches used in this research will serve future comparative research into OA advantage of scientific articles over time and between countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometricsOpen science Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".