Bibliographic record
Abstract
Under the impact of the 2008 financial tsunami, the Ministry of Economic Affairs, R.O.C., made a proposal to the Execution Yuan in March 2009 regarding the renovation plan of Taiwan DRAM industry. This plan was hopefully to establish Taiwan Memory Company (TMC) with private investors to renovate Taiwan DRAM industry. However, under media pressure, in July 2009, the original plan was altered to invite any DRAM company that wished to propose a renovation plan and needed government funding to file an application within three months. In August 2009, Taiwan Innovation Memory Company (TIMC) was officially established. This case aims to discuss the difficulties of Taiwan DRAM industry and the feasibility of the renovation plan through the establishment of TMC by its historical background and potential impacts. The key role of this case is Mr John Hsuan, CEO of TMC, and the decision-making focus is the Renovation Plan of Taiwan DRAM Industry under the charge of the Ministry of Economic Affairs. This covers the fourth quarter of 2008 to the first quarter of 2010. The purpose of the DRAM industry renovation plan was to sustain the technology and the industry, not any individual company. Therefore, the plan did not solve the financial problems of any DRAM companies. Can the DRAM industry renovation plan successfully turn Taiwan's DRAM industry around in a financial crisis and upgrade the competitive advantage of the nation?
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".