Bibliographic record
Abstract
In applying the diversification strategy, a particular company first need to ensure that it have what it takes to venture in related or unrelated business. This is because the high risk involved and great challenges ahead. Problems will occur in different angle. With a floating capital of RM 1 million and paid up capital of RM 15 million, the divestment of the businesses by RS Capital Holdings Berhad had seem to lead in the unprofitable areas when they declared that the company suffers losses o the First quarter of the year. Rather than focusing on the major business which is telecommunication industry, the company had taken a risk in venturing into different industries that they are not experience of. This had caused some of manpower cannot be transferable among the parent companies and the subsidiaries. Information through secondary data, personal interviews and observations that have been conducted, the issues or challenges that need to be highlighted in RS Capital Holdings are in terms of its human resources, capital, company’s management, value chain activities and company’s future prospects. As recommendations, the company could probably shut down the operations for the time being of certain subsidiaries and focusing on which is more profitable base on the internal strength.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".