Lack of Annual Reports Make it Difficult to Analyze Library Strategic Credibility
Bibliographic record
Abstract
Objective – To investigate whether libraries achieve strategic credibility by assessing if strategic planning goals match the achievements described in annual reports. Design – Content analysis of annual reports and strategic plans from a sample of Association of Research Libraries (ARL). Setting – Academic libraries in Canada and the United States of America. Subjects – A random sample of 12 Canadian and 16 American academic libraries. All libraries were members of ARL. Methods – The researcher contacted the directors of 28 ARL libraries and asked for copies of their strategic plans and annual reports. She also visited the websites of libraries to obtain the reports. The contents of the strategic plans and annual reports were analyzed, and trends in the Canadian and American strategic plans were identified. Main Results – This study found that only 39% of ARL libraries produce annual reports, making it difficult to assess if libraries have strategic credibility, as their strategic plans cannot be assessed against annual reports. The strategic plans gathered in this study were analyzed and emerging themes were identified. These included physical library space (renovations, expansions or new buildings); offsite storage; assessment (both of the libraries’ services, and of information literacy training); development activities such as fundraising and marketing; and personnel issues. Cultural differences also were found in the strategic plans, with American libraries being more focused on trends such as digitization and institutional repositories, and Canadian libraries’ plans being more focused on users’ needs. Trends in annual reports were not reported due to the small number of annual reports in the sample. Conclusion – This study gives a snapshot of the trends in strategic plans of ARL members. It shows that many ARL members do not produce an annual report, and that it is therefore difficult to assess if their strategic plans are implemented successfully. The article hypothesizes that the communication of achievements may now be part of development and marketing efforts, rather than traditional annual reports.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.074 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".