Cultural Aspect Management Model for Organization Development of Naval Education Department of the Royal Thai Navy
Bibliographic record
Abstract
This is a qualitative investigation aimed at analyzing the organizational culture of the Naval Education Department of the Royal Thai Navy and determining a cultural aspect management model to decrease the culture gap and promote unity in diversity. Research was conducted over a ten month period from November 2012 to August 2013. The study area was purposively selected and was composed of naval departments in three provinces: Bangkok, Nakhon Pathom and Chonburi. Data was collected by document study and field research. The tools used for data collection in the field were basic survey, participant and non-participant observation, structure and non-structured interview and focus group discussion. There are three major responsibilities of the Naval Education Department (NED). They are: 1) Education and Training; 2) Research; and 3) History. There are six categories of problems with the NED culture: structure, administration policy, leadership, manpower utilization, shared value and morals. The Cultural Aspect Management Model for Organization Development of Naval Education Department of The Royal Thai Navy is the ‘LISIS’ model, which is composed of leadership, inspiration, strategy, identity and shared values.
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.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".