2010 Published by Canadian Center of Science and Education 165
Bibliographic record
Abstract
Numerous researchers argued that the goal of many technology parks and the factors driving innovation success are still a mystery. In addition, it is argued that the problem with analyzing technology parks and cluster building is that recent studies analyze “the most celebrated case studies … to ‘explain ’ their success ” and ignore the less successful ones especially in developing countries. This study uses intensive interviewing to explore obstacles to success of technology parks in Jordan. It identified the following obstacles: 1. absence of a culture of entrepreneurism, 2. lack of autonomy and independence from university officials and government bureaucrats, 3. lack of a critical mass of companies that allows for synergies within parks and, 4. lack of a shared vision among parks ’ stakeholders. The study also found that the education system is unable to instill a culture of entrepreneurism among graduates therefore reducing the number of entrepreneurs and start-ups in Jordan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.818 | 0.579 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".