Identifying and prioritizing the components of the learning city with an emphasis on the lifelong learning
Bibliographic record
Abstract
The learning city has been a new concept aimed at the mobilization of the resources for the learning of all the citizens throughout their lives and in all places to promote the individual and economic development, and the social cohesion. This concept has various dimensions and components, extracted by the researchers and institutions. In this research, the dimensions and indicators of the learning city from the viewpoint of the Canadian Council for Learning, the European Commission and the UNESCO Institute of Lifelong Learning, with emphasis on the concept of the lifelong learning would be investigated. It was a descriptive-analytic research. The research sample included 20 formal education experts and the University professors at the study area of Qazvin city. The final learning city components in five dimensions, including »the formal education system«, »learning in the communities and families«, »learning at work«, »technology and the learning quality« and »the learning culture« have been extracted and analyzed using AHP analysis method and the priorities have been identified in each dimension. The results showed that the most important dimensions of the learning city are the formal learning and learning in the communities and families. Accordingly, the most important components of the learning city are: »the extension of the formal education«, »the use of the trained educators«, »support for the education of the disadvantaged groups«, »encouraging the continuous learning in the family and society« and »learning through the media and cultures« which can be prioritized according to our country's conditions to achieve the learning city.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".