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 aims to identify the current state of knowledge management (KM) diffusion in LIS schools. In terms of content, we have identified two principal approaches to the perception of KM in the LIS community: an active approach, seeing KM as an opportunity for the LIS community to change; and a passive approach, seeing KM merely as a topic of information management with a new label. Our research analyzed study programs at 145 LIS schools and in 188 LIS study programs in the United States, Canada, Europe (in particular, Russia), Australia, India, South Africa, China, Japan, Singapore, and Brazil and observed the inclusion or non-inclusion of KM courses in those programs. We employ a narrower approach to defining a KM course as being one having the term “knowledge management” in its name. The findings indicate that KM courses are integrated in one-third of the LIS study programs analyzed, and in schools with an information science focus this figure can rise to around 45%. Given the importance of this area and various views regarding KM diffusion in LIS schools, we recommend that those who have already implemented a KM course in their LIS programs create an informal community of practice (CoP) on KM implementation in LIS schools and build an open database of lessons learned from such integration, thereby capturing and sharing this crucial knowledge in a single place.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.029 |
| 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 it