Bibliographic record
Abstract
A Review of: Branscome, B. A. (2013). Management of electronic serials in academic libraries: The results of an online survey. Serials Review, 39(4), 216-226. http://dx.doi.org/10.1016/j.serrev.2013.10.004 Abstract Objective – To examine industry standards for the management of electronic serials and measure the adoption of electronic serials over print. Design – Survey questionnaire. Setting – Email lists aimed at academic librarians working in serials management. Subjects – 195 self-selected subscribers to serials email lists. Methods – The author created a 20 question survey that consisted primarily of closed-ended questions pertaining to the collection demographics, staff, budget, and tools of serials management groups in academic libraries. The survey was conducted via Survey Monkey and examined using the analytical features of the tool. Participants remained anonymous and the survey questions did not ask them to reveal identifiable information about their libraries. Main Results – Collection demographics questions revealed that 78% of surveyed librarians estimated that print-only collections represented 40% or fewer of their serials holdings. The author observed diversity in the factors that influence print to digital transitions in academic libraries. However 71.5% of participants indicated that publisher technology support like IP authentication was required before adopting digital subscriptions. A lack of standardization also marked serials workflows, department responsibilities, and department titles. The author did not find a correlation between serials budget and the enrollment size of the institution. Participants reported that they used tools from popular serials management vendors like Serials Solutions, Innovative Interfaces, EBSCO, and Ex Libris, but most indicated that they used more than one tool for serials management. Participants specified 52 unique serials management products used in their libraries. Conclusion – In surveying academic librarians engaged in serials management, the author sought to identify trends and standards in the field, but instead found significant variation in serials budgets and processes amongst the responding libraries. While it is clear that electronic subscriptions are a significant development and now a permanent feature of serials management, decisions to move from print to digital are complex and definitive conclusions about best practices for serials transitions could not be drawn from this study. The survey revealed that institutions have invested in staff and tools for the management of electronic serials, but staffing configurations and tool combinations are also extremely diverse. The author concluded that the lack of standardization in these areas and the disconnect between institution and serials budget size indicated a serials landscape that was highly individualized and customized to each institution’s unique needs.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".