Agile Project Management Facilitates Efficient and Collaborative Collection Development Work
Bibliographic record
Abstract
A Review of:Stoddard, M. M., Gillis, B., & Cohn, P. (2019). Agile project management in libraries: Creating collaborative, resilient, responsive organizations. Journal of Library Administration, 59(5), 492–511. https://doi.org/10.1080/01930826.2019.1616971 Objective – To examine the advantages and obstacles of using Agile (an approach to project management) principles to guide collection development work in ways that allow libraries to better address user needs while increasing transparency and collaboration in their processes. Design – Descriptive case study. Setting – Libraries at a private, R1 university (doctoral university – very high research activity). Subjects – Five cross-disciplinary teams of three to six people, with each team focusing on a separate strategic aspect of library collections work (Communications and Data Visualization, E-Resource Contract Negotiation, Serials Workflow Analysis, Demand Driven Acquisitions, and Serials Budget Projection & Assessment). Methods – The authors facilitated group reflection sessions for the teams to surface outcomes of employing Agile practices and also as a means through which they could learn from their experiences with Agile. The teams engaged in reflection throughout the year-long process where they were asked to share their work, respond to the work of the other teams, and contemplate their own learning and development as a member of a team. Main Results – Using Agile principles to structure and direct collection development work allowed the libraries to meet their stated goals of spending all available funds on relevant materials within the time frame allotted. This style of collaborative work benefitted from recognition of interrelated information needs, willingness to prioritize experimentation over seeking formal training, centering user needs in planning stages, and practicing reflection as a powerful learning tool. Additionally, the authors noted a strengthening of core skills held in high value throughout libraries, such as leadership and project management. Task-oriented skills that included capabilities like data visualization and operational analysis also progressed through learning by working on cross-functional teams. The authors offered guidance for applying these lessons to situations in other libraries that can be generalized to fit other projects. Conclusion – Based on their experiences with adopting Agile practices, the authors offered scalable approaches for implementing Agile that speak to employee buy-in and the overall impact of projects undertaken in this manner. Training that reflects a library’s authentic level of investment in Agile, whether minimal or extensive, is crucial to realizing positive outcomes. The authors also recognized that resistance to change and discomfort with working under transparent conditions will present challenges for many libraries in aligning workflows with Agile methodology. However, Agile did allow for positive shifts toward more investment in shared work on team and individual levels. While failure in Agile projects is more visible and therefore more intimidating, librarians can find themselves able to learn from and correct mistakes more efficiently.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.034 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".