A study on factors related to readership of scientific articles
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
This paper analyzes the relationship between the number of Twitter and Mendeley readers with the article’s subject, publisher, journal, and title length. It also looks at which country has the greatest number of readers to see if researchers can garner more visibility by publishing an article relevant to issues in those countries. The purpose of this report is to help researchers improve the visibility and impact value of their research. The data was gathered from 550,000 scientific research papers published between January 1st and July 1st of 2016. Python’s built-in JSON library was used to extract the number of Twitter and Mendeley readers, as well as the article count for each factor. The correlation between readers per article and each factor was then visualized using bubble graphs, linear regression models, and scatter plots. This paper concludes that the length of the title is the strongest factor affecting readership. In particular, titles with lengths between 51 and 90 characters have the greatest number of readers. Moreover, articles relevant to issues in countries with a higher GDP have the highest overall readership. On the other hand, the publisher and the journal did not have a significant effect on readership, while the subject of the article had a moderate effect on readership.
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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