Cross-border implementation of Institutional Repository: A case of Aga Khan University
Bibliographic record
Abstract
Institutions globally have increasingly embraced Institutional Repositories (IRs) to collect, showcase, archive, and preserve their intellectual and scholarly output. Many benefits are gained from implementation of the platform including: the institution’s visibility, status and reputation is increased; authors get wider public access and visibility thus more citations for their work; long-term preservation of research; and the library benefits from its new role in information creation and distribution thus the opportunity to re-assert its importance in the face of declining user dependence on libraries for simple access to information (Sharif 2013). Despite the high uptake of IRs to manage institutions’ digital resources more effectively, little has been written on the experience of cross-border implementation. This paper seeks to fill this gap by presenting unique lessons learnt from the implementation of Digital Commons (DC), a proprietary hosted institutional repository platform by Bepress. The platform is implemented across AKU’s 7 campuses in 5 countries (United Kingdom, Pakistan, Kenya, Uganda and Tanzania). The varying technological, economical, and cultural contexts of these countries have had effect on the implementation of the platform and have presented some unique and interesting lessons. Cross-border implementation faces many distinctive challenges. From AKU experience, these include lack of a national body for co-ordinating IRs in majority of the countries where AKU is operating; and inequalities in technical expertise, internet access, extent of use, and social support. On the other hand, institutions receive immense benefits from cross-border implementation. Key benefits include: IR helps address the unevenness in availability of researchers’ output where Africa for instance accounts for only 2% of the world’s research output (Christian 2008); and implementation team benefits from networking with colleagues. Being part of the implementation team and working collaboratively with the entire implementation team, the authors share the challenges and best practices learnt first-hand.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".