Smart Cities & 21st Century Economic Development & Welfare Holistic Approach Towards a Roadmap Strategy Development for RAK Emirate
Bibliographic record
Abstract
Modern economic concepts and models are nowadays abounding, thus creating a challenge in keeping pace with the global economic and technological developments, for both institutions and individuals, often failing to grasp the true meanings and purposes of the latter; thus receding chances for economic communities to make use of the sound scientific content therein, fair value, great benefits and implied objectives to achieve economic development and welfare. In recent years, and after the spread of some the concepts such as “sustainability”, “clean and green energy”, "e- government"; the "Smart" concept is nowadays strongly imposing itself in the local scene, after UAE Federal Government and Dubai Emirate have adopted and embarked onto “Smart Progressive” plan implementation. Candid man-of-the-street assumptions would inevitably and systematically link the concept to applications for smart phones and mobile systems and the exploitation of technological resources in everyday life. Whereas, in fact this is only a part of a whole integrated and wide-ranging economic system in which technology plays a pivotal role alongside with several other most prominent and crucial factors. The present paper deals with introduction to the “Smart” economic model implementation in our City of the future, with focus on criteria, requirements, indicators and role and contribution of citizens, government and institutions, aiming at shedding the light onto a hot topic with critical impact on present and future holistic economic action plans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".