Are the most highly cited articles the ones that are the most downloaded? A bibliometric study of IRRODL
Bibliographic record
Abstract
Publication of research, innovation, challenges and successes is of critical importance to the evolution of more effective distance education programming. Publication in peer reviewed journal format is the most prestigious and the most widespread form of dissemination in education and most other disciplines, thus the importance of understanding what is published and its impact on both researchers and practitioners. In this article we identify and classify the leading articles in arguably the leading peer reviewed journals in this discipline. The journal The International Review of Research in Open and Distance Learning (IRRODL) is a peer reviewed academic journal that has been published since 2000. The journal has published between 3 and 6 issues annually with between 50 and 111 research articles per volume. In order to assess the general and the particular impact of highly cited articles this work describes the main bibliometric indicators of the IRRODL journal and these are compared with the total galley views in all formats, PDF, HTML, EPUB and MP3, that IRRODL publishes. In addition to identifying characteristics of the most widely cited articles this research determines if there is a correlation between the articles most highly cited by other publishing researchers and the number of views, indicating interest from both practitioners and research communities. The results show a significant and positive relationship between the total number of citations and the number of views received by articles published in the journal, indicating the impact of the journal extends beyond active publishers to practitioner consumers.
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
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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.087 | 0.122 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".