Institutional Repository Literature: A Bibliometric Analysis
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
The Institutional Repository (IR) concept has given a new dimension to information management in the Internet age. The introduction of an IR can help to redefine the production, dissemination, and the use of resources. This study found that a total of 436 IR research papers published in 118 journals originated from 68 countries. These research papers contain 2,071 citations with an average of ˜4.8 citations per publication. Moreover, out of the total 159 institutions involved in IR research, a majority of them are located in the United States and the United Kingdom. Mainly, out of the fourteen most productive countries eight have recorded TAIs of >100, and six countries recorded TAIs of <100. Most published papers have a single author, i.e., 176 (40.4%), followed by two authors: 152 (34.9%). Interestingly, India, Australia, Canada, Germany, the Netherlands, Malaysia, and Italy have not published any paper with more than five authors. Purdue University has witnessed the highest (˜2) relative citations impact (RCI) on its publications. Elizabeth Yakel from the University of Michigan has published the most papers (7: 1.6%), which have received ˜34 citations. Overall, eight prolific authors have achieved a higher h-index value than the group average.
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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.927 | 0.990 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.008 | 0.002 |
| 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