The role of big data in elucidating learning cities ancient, present and future
Bibliographic record
Abstract
This chapter introduces the concept of the Learning Society as an educational philosophy, which has permeated the ethos of many regions throughout eras of history. Around the world there are a number of discrete initiatives within individual cities or regions, from Canada to South Africa, Australia and beyond, with the most comprehensive developments in the last decade in Asia. The chapter outlines of learning cities, with an overview of exemplars from the past, such as ancient centres of learning, illustrating how learning in antiquity placed an emphasis on the role of place, with implications for culture, health and well-being, social cohesion, inclusion and socio-economic success, as persistent learning city themes throughout the ages. Big Data refers to the complex sources of information created and stored by organisations, including traditional databases, and more technologically diverse sources of data, such as social media, geographical mapping data and weather.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".