An Analysis of Digital Library Publishing Services in Ukrainian Universities
Bibliographic record
Abstract
Abstract Objective – The objective of this study was to assess the current state of digital library publishing (DLP) in university libraries in the Ukraine. The study was conducted in the hopes of gaining a better understanding of the DLP landscape, namely institutional operations, as well as their varying publishing initiatives, processes, and scope. Methods – The current study was conducted from January to June 2017 using a mixed methods approach, involving semi-structured interviews and an online questionnaire. Semi-structured interviews were conducted (n = 11) to gain insight into participants’ experiences with DLP. The interviews helped in the creation of the questions included in our online questionnaire. The questionnaire was distributed to 195 representatives (directors and leading specialists) of university libraries in the Ukraine. Replies were received from 111 of those institutions. The questionnaire consisted of 11 open- and closed-ended questions to allow the researchers to obtain a holistic picture of the process under investigation. Results – Analysis of the 111 questionnaires showed that for 26 libraries, DLP services were performed by employees of a separate structural unit of the library. For 34 libraries, employees of various departments were involved in performing certain types of services. The other 40 respondents’ libraries were planning to do this in the near future. Only 11 respondents replied that they did provide DLP services now nor planned to in the future. Among the libraries providing DLP services, the following results were observed: 54 of 60 work with digital repositories, 47 provide digital publishing platforms for journals, 26 provide digital publishing platforms for books, and 23 provide digital publishing platforms for conferences. Conclusions – The results obtained indicate a growing trend of expanding digital services in university libraries to support study, teaching, and research. Despite the still spontaneous, chaotic, and poorly explored nature of the development of the library publishing movement in the university libraries of the Ukraine, the readiness of librarians to implement publishing activities is notable. At the same time, the survey results point to specific aspects, such as organizational, economic, personnel, and motivational, that require further study.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".