Does Iran Scientific Development Support Its Sustainable Development?
Bibliographic record
Abstract
Over the last few decades, Iran’s scientific development has been on the increase, and it is now the fastest growing country in scientific development, and is currently ranked at 17. Yet there are few papers which address Iran’s sustainable development, and the implications that arise from the development of science. In this paper we therefore aim to answer whether or not Iran’s scientific development supports its sustainable development. To address both parts we have taken data from 1998-2012, and have looked at scientific development, as well as the development of social, economic, and environmental sustainability. What we found was that all areas of sustainability along with scientific development have significantly increased over the years, with subject areas such as Medicine, Chemistry and Engineering producing the highest number of publications in 2012. As well as this, the data clearly shows that all areas have followed a similar trajectory, and show similar percentage changes year on year. Looking at the data alone we can say that Iran’s scientific development does support sustainable development, however this is not the case when we include performance data. Both sets of data show that all areas of sustainability and science in general have increased over the last 15 years, however they differ with regards to the areas in specific. These differences however can be attributed to many points. Further research could look into finding a more robust way of defining the output of research, using a different approach to categorise subject areas, and collecting qualitative data to support the data collected.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".