Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it