Kacang Cerdik: A Conceptual Design of an Idea Management System
Bibliographic record
Abstract
An idea management system is where ideas are stored and then can be evaluated and analyzed. It provides the structure and the platform for users to contribute ideas for innovation and creativity. Designing and developing an idea management system is a complex task because it involves many users and lot of ideas. Some of the critical features for an idea management system are supports for the ideas generation process, stimulate users to actively participate, facilitates reuse of ideas and encourages open collaboration among the users. This paper discusses a conceptual architecture design of an idea management system called Kacang Cerdik based on three novel concepts; actor, object and workflow. In the system, an idea is an object or an entity that can be manipulated by the users. An idea as an object can be used to generate other ideas. It can also derive its features from other ideas. In this system, an idea is linked with other ideas in the system. The idea will flows from one state to another state in a single workflow. A single workflow is a process or a pipeline that consists of many states. At each of the states, an idea (an object) can manipulated by a user (an actor) based on his current role. The single workflow consists of five different states which are editing, collaborating, reviewing, publishing and evaluating. In the system, a user is an actor who has multiple roles such as a submitter, a reviewer or an evaluator. Based on this concept, a user can submit a new idea or can review an existing idea or can vote on an idea. The system also uses the concept of crowd wisdom to evaluate the ideas. Finally, the potentially viable ideas at the end of the workflow or the pipeline can be further evaluated in a new product development cycle. This conceptual design encourages open collaborations among the users, reuse of previous ideas and use of crowd wisdom in ideas evaluation. A working prototype called Kacang Cerdik has been developed using these concepts as a proof-of-concept.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".