The Library Assessment Capability Maturity Model: A Means of Optimizing How Libraries Measure Effectiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Objective – This paper presents a Library Assessment Capability Maturity Model (LACMM) that can assist library managers to improve assessment. The process of developing the LACMM is detailed to provide an evidence trail to foster confidence in its utility and value. Methods – The LACMM was developed during a series of library benchmarking activities across an international network of universities. The utility and value of the LACMM was tested by the benchmarking libraries and other practitioners; feedback from this testing was applied to improve it. Guidance was taken from a procedures model for developing maturity models that draws on design science research methodology where an iterative and reflective approach is taken. Results – The activities decision making junctures and the LACMM as an artifact make up the results of this research. The LACMM has five levels. Each level represents a measure of the effectiveness of any assessment process or program, from ad-hoc processes to mature and continuously improving processes. At each level there are criteria and characteristics that need to be fulfilled in order to reach a particular maturity level. Corresponding to each level of maturity, four stages of the assessment cycle were identified as further elements of the LACMM template. These included (1) Objectives, (2) Methods and data collection, (3) Analysis and interpretation, and (4) Use of results. Several attempts were needed to determine the criteria for each maturity level corresponding to the stages of the assessment cycle. Three versions of the LACMM were developed to introduce managers to using it. Each version corresponded to a different kind of assessment activity: data, discussion, and comparison. A generic version was developed for those who have become more familiar with using it. Through a process of review, capability maturity levels can be identified for each stage in the assessment cycle; so too can plans to improve processes toward continuous improvement. Conclusion – The LACMM will add to the plethora of resources already available. However, it is hoped that the simplicity of the tool as a means of assessing assessment and identifying an improvement path will be its strength. It can act as a quick aide-mémoire or form the basis of a comprehensive self-review or an inter-institutional benchmarking project. It is expected that the tool will be adapted and improved upon as library managers apply it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.352 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it