Can Knowledge Management Be Appropriate for Shipbuilding?: Based on Typology and the Seven C’s Model
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
I use typology to find the divisions of knowledge management by literature review, then I use the seven C's model to evaluate knowledge management in shipbuilding. The research aims to promote the knowledge management practice in shipbuilding. In the meantime, the research method can be a reference for future research on knowledge management in other fields. From the evaluation of the seven C’s model, we can see that six aspects of the implementation of knowledge management are suitable for shipbuilding. But in the connection part, we realize that if we want to keep a strong connection with all the people in shipbuilding, it is very difficult. The limitation of our research is that we rely on the literature review and our own experience to evaluate. It will be meaningful to conduct empirical research in the future to fill up the gap between the academic part and the practice part. This article can help shipbuilding organizations realize that conducting knowledge management is appropriate for shipbuilding. In the meantime, it reminds us what we need to do if we want to have effective knowledge management in shipbuilding.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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