A Study of Knowledge From Closed and Open System That Affect the Innovation Capability of Employees in the Thai Automotive Industry
Bibliographic record
Abstract
The objective of this research was to investigate the knowledge from closed and open systems that affect the innovation capability of employees in the Thai automotive industry. The study was conducted by reviewing related literature and theories and holding a small group meeting with experts in the automotive industry to review the research model and factors obtained from this study. This research is only part of the main research that we are currently studying. The results from this research have led to the research model. According to the research results, knowledge from a closed system can be divided into two types: 1) Knowledge from on-the-job training that consists of six factors; i.e. Coaching, Mentoring, Job rotation, Job instruction, Apprenticeship, and Understudy, and 2) Knowledge from off-the-job training that consist of one factor, i.e. Conference and seminar. In addition, knowledge from an open system can be divided into five factors, i.e. Free open software, Business partnership, Customer knowledge, Supplier knowledge, and University knowledge. The results obtained from this research will be used to additionally expand the development of research model in order to study the population, collect data, and extend results of the next research.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".