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.
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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.221 | 0.302 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".