UNESCO MIL Cities Network As Opportunity for Development in Africa
Bibliographic record
Abstract
The main objective of this article is to show the opportunities that can open up for African cities from the UNESCO MIL Cities approach. The methodology used was the theoretical-practical based on the bibliographical review and the narrative of the authors' experiences. MIL Cities is a UNESCO framework that speaks of the need to build or reform urban spaces so that they use new technologies but using them ethically and respectfully for vulnerable groups, transcending cultural barriers to communication and contributing to the objectives of the 2030 agenda. The article has three parts. The first part insists on the definition of the MIL Cities concept, its origin and the preliminary work that has been done to support its implementation. An exhaustive explanation is given of the 13 Indicators and 252 metrics of MIL Cities published by Chibás Ortiz and other authors. The creation of the UNESCO World Network of MIL Cities is discussed. The second part explains the role of metrics in the evaluation of MIL projects in towns and cities. This second part focuses also on the practical implementation strategies and cases deployed to spread the concept and its objectives. A particular emphasis is placed on the various webinars organized and their format though the whole world. This part summarizes actions and events taken to promote the initiative. The third part focuses on the Latin America & Africa MIL Cities initiative. In this last section, the article focuses on the launch of the project and on the objectives to be achieved to develop MIL Cities in Africa. It highlights the countries involved in the project launch activities, the promotional strategies to be deployed to disseminate the concept to all countries on the continent. It is concluded that the MIL Cities framework opens up new possibilities for the growth and development for African cities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".