Managing the Electronic Resources Lifecycle with Kanban
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
This paper discusses the implementation of Kanban as the framework for managing electronicresources workflows by presenting case studies from the University of Saskatchewan Library and at theSaskatchewan Polytechnic Library in Saskatchewan, Canada. Librarians at both institutions independentlychose to adopt Kanban to manage electronic resources work, applying the essential Kanban frameworkof lists titled to do, in progress, and done. Examining the similarities and differences in each librarian’sexperience and discussing two different software programs used, we have included descriptions of ourimplementation, in-depth information about the origins of Kanban, and its more recent applications totechnical work. We found numerous benefits—including reduced email communication and improved duedate tracking—to our implementation of Kanban and no significant drawbacks. Interest in applicationsof Kanban in libraries is on the rise, and we found there were significant benefits of using Kanban forelectronic resources teams when used in conjunction with other tools (e.g., spreadsheets, email, ERMS).
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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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