Developments in Practice XXII: Expertise Location and Management: Hope or Hype?
Bibliographic record
Abstract
The idea that KM can help people in large, often widely geographically dispersed organizations find out who has subject matter expertise or who knows how and where to get at important know-how is intrinsically appealing to knowledge managers. After all, helping people access the knowledge they need is fundamentally what KM is all about. Early approaches to expertise location and management (ELM) typically built skills repositories, but these have not been successful in accomplishing these objectives. As a result, expertise location and management is now on the steep downward slope of the KM "hype cycle" in most organizations. This paper explores the question of whether ELM is an idea that is evolving and maturing and which will ultimately deliver on its promised value, or whether organizations should simply give up on the idea as being not worth the effort. To explore this issue in more detail and to better understand how organizations are conceptualizing and implementing this specific KM initiative, the authors convened a focus group of practicing KM managers from a variety of organizations. This paper first situates the topic of expertise location -- where it fits in with other KM issues and also how our understanding of this topic has evolved over time. It next describes some of the benefits and challenges of ELM. Following this, it explores several different approaches to these types of initiatives. From these, we derive a number of principles for effective ELM implementations. Finally, we present some practical advice for managers who are considering using ELM in their organizations. The paper concludes that ELM has the potential to be a "killer app" for KM, but only if it can be focused and designed effectively to appropriately integrate technology with human facilitation.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".