1 Strategic Challenges Facing Research Libraries
Bibliographic record
Abstract
I am pleased to have this opportunity to bring a North American perspective to these discussions. We have struggled to find coherent and constructive responses to the challenges facing universities and libraries as they engage an increasingly turbulent and exciting environment. My paper is intended to provide a framework for discussing the strategic challenges facing research libraries. This framework is based on several premises. First, I suggest the traditional library mission is in jeopardy and that it needs to be rethought and redirected. We can no longer be comfortable with a traditional interpretation of what constitutes a library. Second, I believe the current scholarly communication system is not sustainable. Skyrocketing costs of information resources, new formats for these resources, and a declining investment in the traditional publishing and library models create irresistible pressures forcing change. Libraries need to strategically position resources and services. New publishing models must be developed. After exploring these premises, I will suggest 10 challenges presented by the need to rethink and reengineer the systems of scholarly communication. In order to get the coherent, constructive changes needed, library leaders have to take an active role in shaping the future collaboratively. Background Let me start with a few comments about ‘The Association of Research Libraries ’ (ARL) to help you understand our approach to these issues. ARL is a not-for-profit membership organisation comprising the leading research libraries in North America. Its mission is to shape and influence forces affecting the future of research libraries in the process of scholarly communication. ARL programs and services promote equitable access to and effective use of recorded knowledge in support of teaching, research, scholarship, and community service. The Association articulates the concerns of research libraries and their sponsoring institutions. ARL is currently comprised of 124 institutions and 16 of those are Canadian. Most of those institutions are academic Research Libraries, but we also
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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.040 | 0.022 |
| Scholarly communication | 0.091 | 0.048 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.030 | 0.022 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".