Bibliographic record
Abstract
Purpose This study aims to review the current level of service and support provided for the bibliographic management applications, EndNote and RefWorks providing research libraries with a baseline for benchmarking their support of these tools. Design/methodology/approach The 111 web sites of the Association for Research Libraries (ARL) academic libraries were systematically reviewed in 2009 for any information provided on the support for bibliographic management applications. This information was recorded and examined by type of library, US publicly supported college or university, US private college or university, or Canadian college or university. Findings The majority of ARL libraries provide support for one or more of these applications with the largest percentage, 42 percent, providing support for both applications. The US privately supported colleges and universities were more likely to have licensed an application and to provide support for both applications. A large percentage of libraries provide instruction on the supported application and instructional materials. The libraries supporting RefWorks were more likely to use the instructional materials provided by RefWorks where more libraries produced supporting materials for EndNote in‐house. Practical implications Examples of extemporary libraries supporting EndNote, RefWorks and both applications are provided along with other information to aid in the design and or development of support for bibliographic management applications. Originality/value This is the first large academic library study examining the overall support provided for these important and widely supported applications designed to work with library resources to aid in the research process.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".