Sharing Success: A Review of Strategic Planning, Annual Reports, and Publicly Available Information from Academic Libraries
Bibliographic record
Abstract
Objective – This paper reports on a study which explored web-based information sharing practices in North American academic libraries. This study specifically focused on how selected academic libraries use data, reports, and other strategic planning documents to communicate success and demonstrate impact to stakeholders, administrators, and peers. Methods – An environmental scan was conducted to explore the assessment programs and communication practices of 97 North American academic libraries. The population for this study was identified on the basis of several metrics: consortial membership, Association of Research Libraries (ARL) ranking on various criteria, and institutional attendance at the 2014 and 2016 Library Assessment Conferences (LAC). Researchers conducted content analyses on the websites of the 97 libraries to identify measures of institutional support for assessment and to explore the range, depth, and quality of data made available. These iterative analyses were supported by the use of a rubric developed based on emergent criteria observed during multiple phases of review. Results – Of the libraries reviewed, 57% made some form of data available to the public. The most robust and effective use of data observed in this study involved the use of data to tell stories about the library and its impact. While this study found a positive correlation between libraries with clear investments in assessment and their use of data in public documents, it found that other factors such as an institution’s consortial memberships or funding model may more strongly influence a library’s decision to make data available. Conclusions – While observations gleaned from this study may serve as a benchmark for evaluating communication practices in academic libraries, further research is necessary to understand how factors within an academic library, its parent institution, or the profession at large may contribute to this decision making 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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.058 | 0.074 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".