Organizational knowledge generation: lessons from online communities
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
Purpose – Knowledge capturing and sharing within an organization have been extensively studied in the literature. In this stream of work, an influential focus is on the process of encoding and managing knowledge to enable effective reuse within the organization. With the advancement of internet and web technologies, there is an increased interest in the study of knowledge flows in online communities. The authors highlight in this paper the fact that the boundaries between internal and external organizational knowledge are disappearing, mainly due to the extensive use of online-based platforms to support organizational operations. The authors believe that this will affect the activities of knowledge management in today’s businesses. The purpose of this paper is to provide guidelines for organizations on how to bridge their internal and external knowledge using an integrated semantic approach. Design/methodology/approach – In this paper the authors review two classes of approaches, those that target internal organizational knowledge, and those that target online knowledge flow processes. Then the authors identify the challenges involved in today’s knowledge environments. To address those challenges, the authors propose a framework to bridge and integrate internal and external organizational knowledge. The authors map the activities handled in the framework to the existing knowledge management activities identified from the literature, and highlight how emerging technologies are used to support such activities along the knowledge management process. The authors apply the approach in the context of an organization’s process that heavily depends on the appropriate alignment of internal and external knowledge. The authors focus on the use of emerging technologies that support collaboration and the generation of explicit and reusable semantics. Findings – Interaction points within organizations can be used to define the scope of knowledge exchanged. Following a methodology around the proposed framework, it is feasible to create conceptual connections around internal and external knowledge through explicit semantics. Such connections that are created to support online communities’ knowledge exchange can be applied to internal organizational knowledge, and used as a bridge to external knowledge sources. Originality/value – The paper provides a roadmap for organizations on how to manage organizational knowledge processes in a coherent and collaborative semantic platform, with a view to what is available outside the boundaries of an organization.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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