Higher education and the global marketplace from Malaysian experience
Bibliographic record
Abstract
Internationalisation and globalisation concepts in higher institution is now well established as a global phenomenon, especially in the major-English speaking nations such the USA, the UK, Australia, New Zealand and Canada (see Hemsley-Brown and Oplatka, 2006). Majorities of the higher institutions recognize that they need to market themselves in a climate of educational international competition. As the competition among them in the higher level and intensive activity, they increasingly behave as business entity by adopting more business strategies. In recent years, they have to promote the acceleration of international linkages, brand campuses, single purpose programs and other forms of transnational education and quality of education for the customers. HEIs that are able to learn about customers, competitors and regulators stand a better chance of sensing and acting upon events and trends in the marketplace (Tippins and Sohi, 2003). However, the task for all activities mentioned above is not always easy to remain competitive in the eye of customers at global environment. Other variables such as government intervention, international law, different custom procedures, variety of languages, foreign exchange, different costs, different behaviour and life styles became the challenges for higher institutions to providing a quality and sustainable education program. Paramewaran and Glowacka (1995) in their study of university image found that, higher education institutions (HEIs) need to maintain or develop a distinct image to create a competitive advantage in an increasingly competitive market. In a number of countries, governments have been at pains to stress the economic benefits resulting from higher education (Yorke, 1999). As we know, the education market is worth hundreds of billions of dollars in todays markets, and every week sees a new joint venture announced by traditional or new players all over the world, as they jockey for position in this increasingly global market. Malaysia in the 1980’s recognized that it would be unable to educate its own 6 percent of its population through its own institutions and began to partner with international institutions to supplement its system of higher education (Lenn, 2000). Further more in recent years, this move has led to the government invited foreign universities to operate on Malaysian soil. With the move, the government made another important decision, to turn Malaysia into an educational hub in the region. For both public and private HEIs, they have to take more ownership and responsibilities for the overall products and services that they offered to the customers. Therefore, many HEIs already adopted some form of business strategies especially in marketing for their strategic activities in their operations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".