Bibliographic record
Abstract
An increasing number of mayors are adding ‘learning’ to traditional city responsibility for sewers, roads, parks, garbage and dog-catching. However, just like in the 1970s, there is uncertainty about what is meant by ‘learning’ and considerable variance embedded in the notion of ‘city’. Some Learning City advocates look to UNESCO’s Faure and Delors reports for advice on how to interest citizens in learning in informal, nonformal and formal settings. Hence, older preoccupations (such as the learning society) are enjoying a discernible renaissance. Other authorities depend on OECD (money-oriented) notions of learning. In China, there is an infatuation with Peter Senge’s notion of ‘learning organisation’. In this paper, the task is to examine the Learning City as a place and a process, analyse the political orientations and scope of well-known Learning Cities and reflect on UNESCO Learning City world conferences held in Beijing, Mexico City and Cork. Finally, the author highlights scholarly issues needing research and urges adult educators and advocates of lifelong learning to get involved with Learning Cities before their places are nabbed by ‘smart city’ enthusiasts raving about digital literacy and entrepreneurs motivated by the smell of money.
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".