Bibliographic record
Abstract
Board Game conceived for the WUF in Kuala Lumpur. We have used it in different training and involving various stakeholders. \nInvolving policymakers and civil servants in the field of: \n1) Capacity Building organised with UNHabitat, Regional and Metropolitan Planning Unit Urban Planning & Design Branch in Uganda with USMID and the Municipalities of the West Nile Region. The reference person at the time was Stephanie Loose who still uses it during the workshops she organises as project manager of the Human Settlements Officer Global Solutions Division Department | Programme Development Branch (you can also asking her for some suggestions: stephanie.loose@un.org); \n2) TELLme training in the frame of a co-funded EU Erasmus +: Tellme project, Training for Education, Learning and Leadership towards a new MEtropolitan discipline. In Guadalajara (Mexico) and Mendoza (Argentina), Seville (Espana). During these Metropolitan Training, involving HE faculties within the policymakers. Through the MetroGame introduction, they improved their competencies needed for working in the Metropolitan field more accurately and proficiently. Nevertheless, thanks to the knowledge of new prospective and techniques and some legal and regulatory expertise necessary to achieve excellence in their work performance. \n \nWe also used it in different Master and Bachelor Courses at Polimi and Guadalajara Universities, and in some Postgraduates Masters at Montreal and Etsas (Seville) Universities. \n \nOur idea, which we have not yet realised, was to create a virtual game after the success of the board game. The idea is to create a common platform on which we can then insert all the contents we want. For example, we could introduce your theme of urban-rural linkage, shaping the game contents as to figure out a public strategy for the invention of a new Urban-rural linkage pattern. A candidate for mayor of an Italian city asked us to produce the contents of the game according to possible urban transformations and so on. \n \nAn interactive game would allow us to change the scales to verify the difference in relevance of each element of the territory as the size of the contest changes; to introduce other actors and above all, as I have just mentioned, to interchange the contents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads 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".