Investigation of challenges in academic institutional repositories
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
Purpose The purpose of this paper is to explore the breadth of the challenges and issues facing institutional repositories in academic libraries, based on a survey of academic librarians. Particularly, this study covers the challenges and barriers related to data management facing institutional repositories. Design/methodology/approach The study uses a survey method to identify the relative significance of major challenges facing institutional repositories across six dimensions, including: data, metadata, technological requirements, user needs, ethical concerns and administrative challenges. Findings The results of the survey reveal that academic librarians identify limited resources, including insufficient budget and staff, as the major factor preventing the development and/or deployment of services in institutional repositories. The study also highlights crucial challenges in different dimensions of institutional repositories, including the sheer amount of data, institutional support for metadata creation and the sensitivity of data. Originality/value This study is one of a few studies that comprehensively identified the variety of challenges that institutional repositories face in operating academic libraries with a focus on data management in institutional repositories. In this study, 37 types of challenges were identified in six dimensions of institutional repositories. More importantly, the significance of those challenges was assessed from the perspective of academic librarians involved in institutional repository services.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.043 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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