Bibliographic record
Abstract
The modern Learning City concept emerged from the work of OECD on lifelong learning with streams of Learning Cities and Educating Cities having much in common but having little contact with each other. While the early development of Learning Cities in the West has not been sustained, the present situation is marked by the dynamic development of Learning Cities in East Asia - especially in China, the Republic of Korea, and Taiwan. In this context, the paper discusses the evolution of three generations of Learning Cities since 1992 and speculates on the future. The experience of the first generation is discussed in terms of development in the UK, Germany, Canada, and Australia where initiatives, with some exceptions, have not been sustained. Beijing and Shanghai are discussed as examples of the innovative second generation in East Asia, which is seen as a community relations model in response to the socio-economic transformation of these countries. International interest in Learning Cities has now been enhanced following a major UNESCO International Conference on Learning Cities in Beijing in October 2013, which is to be followed by a Second International Conference in Mexico City. The Beijing Conference adopted the Beijing Declaration on Learning Cities supported by a Key Features document. The paper speculates on possible future development post Mexico City, including the situation in Australia, which is seen as opening opportunities for innovative initiatives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.011 |
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".