Top universities, top libraries: do research services in academic libraries contribute to university output? 37th IATUL Conference, 5-9 June 2016 "Library Leadership in a Sea of Change", Halifax (Canadá)
Bibliographic record
Abstract
University context is nowadays mostly characterized by the implementation of competitiveness and cost-effectiveness criteria. There are two main characteristics of the new management model: a new relevancy to the university funding and the predominance of the research criteria as excellence indicator. Evidence of the growing role of research in universities are the parameters to rank the excellence of higher education institutions, such as ARWU (Academic Ranking of World Universities) of the University of Shangai, SIR (SCImago Institutions Rankings) or the THE (Times Higher Education World University Rankings). The research orientation also imposes to the academic library, with the growing implementation of services to support research. Evidences are at last reports about trends in academic libraries by ACRL (Association of College & Research Libraries), especially at the latest edition: The 2015 Environmental Scan of Academics Libraries. The international survey Bridging the Librarian-Faculty Gap in the Academic Library (2015) also emphasizes the greater impact and relevance of the academic library to research, stressing the perception of the library as essential in th is process. This paper tries to establish a connection between excellent universities and the research oriented services by their libraries. Our research hypothesis is: the universities at the top of the rankings have libraries that provide excellent services to support research processes. Ten of the top universities at the ARWU and Times rankings are used as sample and their libraries services analyzed. As research method we use d the observation of the selected libraries webpages, with a checklist where the most relevant services to support research processes are identified.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".