The Irish Research electronic Library initiative: levelling the playing‐field?
Bibliographic record
Abstract
Purpose The Irish Research electronic Library (IReL) is a nationally funded electronic research library providing online access to full text articles from 1,000s of peer‐reviewed publications in a range of disciplines. The aim of this paper is to examine the opportunities that have arisen for academic libraries at a local level in terms of how they expose resources and promote the initiative. It discusses the challenges that have arisen as libraries enhance, or indeed introduce, value added services to their research community. It examines the results of an in‐depth national survey, which yielded invaluable insights into how Irish researchers were using library services. Finally, it reflects on the challenges libraries face in facilitating and nurturing research behaviour. Design/methodology/approach In the first quarter of 2007, seven university libraries asked their researchers for feedback on how they use IReL resources and their awareness of the initiative in the form of a national survey. These results and in particular the feedback from DCU researchers are further analysed. Focus groups and visits to research centres also provided more in‐depth analysis. Findings The paper finds that a collaborative approach to the negotiation of a single national licence for seven academic libraries, with associated training and a discount for consortium contracts, has been highly successful. However, it has also posed significant challenges for all libraries in terms of ensuring that the resources are fully exploited and that the necessary support structures are in place to facilitate the provision of appropriate services to the growing research community. Originality/value The paper will be useful to libraries planning services for fourth‐level researchers and in particular, services that promote access to online resources.
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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 | 0.048 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.050 | 0.035 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".