Reorganizing a Technical Services Division Using Collaborative Evidence Based Information Practice at Auraria Library
Bibliographic record
Abstract
Objective - The objective of this article is to demonstrate the efficacy of collaborative Evidence Based Information Practice (EBIP). Methods - Application of theoretical frameworks of shared leadership, appreciative inquiry, and knowledge creation to propose an organisational effectiveness model. Results - The Auraria Library case study demonstrates the introduction of a collaborative EBIP culture – reorganizing personnel, reassigning responsibilities, and measuring outcomes – successfully within a technical services division. By doing so, participants are encouraged and empowered to identify problems and create solutions amidst a dynamically changing electronic resources environment. Conclusions - Auraria Library’s technical services department created a collaborative EBIP environment by flattening workplace hierarchies, distributing problem solving and encouraging reflective dialogue. Embracing the collective knowledge and experiences of Technical Services staff members enables them to be valued and respected leaders and followers.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".