Bibliotecas universitárias e serviços de apoio à pesquisa: uma revisão sistematizada da literatura
Bibliographic record
Abstract
The article presents a systematized literature review to investigate the research support services offered by university libraries over the years. The survey was conducted in the second quarter of 2023, using the databases Library and Information Science Abstracts, Information Science & Technology Abstracts, Library, Information Science & Technology Abstracts, Brapci, Web of Science, Scopus, and Dialnet. The survey resulted in 55 case studies or reports of experience in research support services. The majority were services related to research metrics and impact, research data services, and training and education. Some services still fit into traditional models, including lending and collection consultation. Academic publishing services were also frequently mentioned. Special attention was given to the reference to embedded librarians. Research data services had a significant and diverse offering, mainly of a consultative nature with a focus on technical services such as data storage. Additionally, some more robust data services were identified, although they were scarce, such as data computing services. Of the studies related to research data, the majority came from libraries in Australia, the United States, the United Kingdom, Canada, and New Zealand. It is concluded that the offered research support services are linked to changes in the way science is conducted, especially in the emerging field of data-intensive science. There was a trend of growth in studies, with a slight increase between 2019 and 2023, indicating that the theme of research support services is emerging in university libraries.
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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.065 | 0.106 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".