Bibliographic record
Abstract
Database usage data from a random sample of ninety-eight public libraries and library systems in the United States and Canada reveal patterns of use. Library users at all sizes of public libraries tend to use research databases most frequently early in the week, at midday, and at times that correspond to the academic calendar (November in this six-month sample.) Peak usage varies with size of library, but a capacity of between one and ten simultaneous users will satisfy 99 percent of demand in every size of library. A questionnaire sent to these libraries revealed many other factors that might influence database use, including posting signs or preparing handouts, availability of remote login, and placement of a database on the library's homepage. Only the number of workstations, adjusted for population, was found to be statistically correlated with amount of use. Public librarians often find themselves negotiating complex licensing agreements when selecting fee-based digital resources for their libraries. Resources that will be offered online through a library may be priced by vendors in a variety of ways. A popular pricing scheme for public libraries involves negotiating a price that depends on the number of users allowed online at any one time on any one database. This simultaneous (or concurrent) use pricing scheme allows libraries to keep costs down and pay only for the number of users likely to be needing a database at one time. The success of this pricing scheme depends on accurate estimates of how many simultaneous users should be supported. If too few are supported, users get frustrated by system busy signals; supporting too many simultaneous users results in unnecessarily expending scarce resources on higher fees. Predicting likely numbers of simultaneous users is especially difficult when there is no history of prior usage. The library that is leasing a new product or offering online access for the first time must often guess about how much each database will be used. Usage data from other public libraries may, however, help similar libraries to predict levels and patterns of use. Other factors such as how many workstations are available in a library and whether or not the library allows remote access may also complicate this picture. A two-phase study of public libraries helped identify patterns of database use, levels of simultaneous use, and what factors might influence this use. Online data captured from ninety-eight public libraries reveal (1) how many users are logged on simultaneously to selected online research databases and (2) the time of day, week, and month when users are searching the most. Examination of these data may help other libraries negotiate simultaneous usage licenses and estimate the number of workstations and ports required. Usage data do not show, however, what each individual library is doing, if anything, to encourage use of databases. In a supplemental survey, each library for which usage data were gathered was asked about its specific environment for online access, and information was gathered about factors that may influence online use. Review of the Literature Historically, usage studies were used to predict an appropriate number of chairs to provide in the library or to adjust staffing schedules to correspond to peak times. Later studies helped libraries determine how many terminals were required for their new online catalogs. A 1983 report incorporated queuing models to recommend appropriate numbers of terminals for online catalogs.[1] Turnstile counts have been used to optimize reference department staffing or pickup schedules for shelving. Turnstile counts show that peak usage periods in academic libraries correspond to the academic calendar and daily class schedules.[2] Patterns or levels of use from within a library may be different from remote use patterns. The New York Public Library Research Libraries compared patterns for remote usage of their OPAC with patterns of usage from within the libraries. …
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".