Enhancing business entrepreneurship through open government data
Bibliographic record
Abstract
Small and medium enterprises play a very important role in today’s economy and contribute heavily on a national economy specially in developing countries like Jordan, but they are still facing many challenges, and need support from government specially in data availability. This study aimed to know the impact of open government data (OGD) on business entrepreneur-ship from Jordanian Irbid State entrepreneurs’ point view. To achieve this, (600) questionnaires were distributed to the sample of Jordanian entrepreneurs in Irbid state and (536) valid questionnaires were recovered. The study indicates that OGD and entrepreneurship had a moderate level. There is a significant statistical effect of OGD on business entrepreneurship (α ≤ 0.05) in Jordanian SMEs Irbid state. At the end researchers recommend government and entrepreneurs to adopt OGD as a strategy to maximize benefits gained from open government data strategy, improve the quality of published data, and recommended entrepreneurs to increase the level of relying on OGD to get information about materials, markets, competitors, legislations and so on to get benefits for their companies.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".