E-Community System towards First Class Mentality Development: An Infrastructure Requirements Analysis
Bibliographic record
Abstract
E-Community portal can be classifed as an extension of normal type of knowledge management system (KMS) development towards first class mentality. It servers varities of expects in term of capabilities and services especially for the benefits of community. Most of the community today are looking on this matter as a very important issue and try to search the best way to manage or organize this community system for sustain a high rate of continuous improvement. While e-community system (ECS) or portal is a system that related to the process of knowledge capture, re-use, searching and representation to the user in a variety of form. The role of system could be determined by looking on the issues on how knowledge can be applied at the right time in the faster ways that based on the simplest command or agent given to the system in order to get the relevant knowledge from the portal. Besides that, system also could be looked on how the best element of infrastructure requirement could be used for, in the benefits of users in order to stored and captured as well as presenting the knowledge portal. The paper presents the analysis of the ECS infrastructure requirement, and its system implementation in a community of practise (CoP) especially towards first class mentality development as well as discussing a variety issues that related to its involvement, so that it will help CoPs to increase their productivity and quality as well as to gain return on investment (ROI).
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".