Bibliographic record
Abstract
Embracing knowledge management (KM), or at least learning how to align one’s work with knowledge management vocabulary and processes within an organization, can prove beneficial to librarians whether they are working inside or outside of a library setting. For library and information science (LIS) professionals seeking opportunities outside of library settings, knowledge management projects, which may be led by teams from a variety of disciplinary backgrounds, provide an opportunity that matches the skillset they have developed through their LIS education or through employment experience in a library. For libraries, particularly special and corporate libraries trying to articulate their value to funding or strategic decision making bodies, repositioning the work the library does in terms of knowledge management may prove beneficial as it allows the library to demonstrate its potential contributions to organizational goals and its ability to directly help business units. This article provides a brief introduction to knowledge management for LIS professionals who are unfamiliar with the concept or practice, identifies some barriers that have prevented libraries from engaging in KM activities in the past, outlines the competencies that are required to practice KM, and provides some directions on how LIS professionals can develop these competencies. The article provides readers interested in pursuing opportunities in knowledge management with the background information they need to get started.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.045 |
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".