The National Site Licensing of Electronic Resources: An Institutional Perspective
Bibliographic record
Abstract
While academic libraries in most countries are struggling to negotiate with publishers and vendors individually or collaboratively via consortia, a few countries have experimented with a different model, national site licensing (NSL). Because NSL often involves government and large-scale collaboration, it has the potential to solve many problems in the complex licensing world. However, not many nations have adopted it. This study uses historical research approach and the comparative case study research method to explore the seemingly low level of adoption. The cases include the Canadian National Site Licensing Project (CNSLP), the United Kingdom's National Electronic Site Licensing Initiative (NESLI), and the United States, which has not adopted NSL. The theoretical framework guiding the research design and data collection is W. Richard Scott's institutional theory, which utilizes three supporting pillars—regulative, normative, and cultural-cognitive—to analyze institutional processes. In this study, the regulative pillar and the normative pillar of NSL adoption—an institutional construction and change—are examined. Data were collected from monographs, research articles, government documents, and relevant websites. Based on the analysis of these cases, a preliminary model is proposed for the adoption of NSL. The factors that support a country's adoption of NSL include the need for new institutions, a centralized educational policy-making system and funding system, supportive political trends, and the tradition of cooperation. The factors that may prevent a country from adopting NSL include decentralized educational policy and funding, diversity and the large number of institutions, the concern for the “Big Deal,” and the concern for monopoly.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".