Geoskills Among Academic Librarians in Greece, Cyprus and Spain
Bibliographic record
Abstract
Existing literature on Geographic Information (GI) and libraries points out that American and Canadian librarians have long been aware of the intimate relationship between geospatial data and libraries. In Europe, though, there is almost no literature to this regard, and academic libraries that offer GI services are the exception. Despite the fact that European and National institutions are putting lots of efforts forward for making open data freely available to society, and for supporting full programs to generate business out of it, this paper examines why libraries and librarians are not perceived as key players in the (geo) data-driven economy. Starting with a survey addressed to academic librarians (in three European countries: Greece, Cyprus and Spain) about their GI knowledge and skills, the paper attempts to shed some light on the librarians' perception about their role in GI management, and to identify to what extent they are ready for providing GI services to their communities. The ultimate goal of this paper is to serve as a triggering factor to wake up European academic libraries, Librarianship programmers and librarians themselves, to encourage them to look for opportunities in geospatial data management.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".