Locating creativity in the city using Twitter data
Bibliographic record
Abstract
This study applies a new methodology using the location of tweets from creatives to study where economic creativity takes place in a city. Based on a Twitter network in Brighton and Hove (United Kingdom), a creative hub, we identify freelancers and entrepreneurs in the creative industries that form the ‘core’ of the ‘creative class’ but have rarely been captured in existing spatial research. We use a comprehensive geodatabase of ‘Points-of-Interest’ and Census of Population residence and workplace locations to match tweets with types of places. Findings show that practices of economic creativity are less spatially clustered in central parts of the city and more spatially distributed across the city than studies that used business register data or cluster approaches suggested. Residential areas, which proxy for home locations, have a high incident of creative activities besides urban amenities and coworking spaces. It is concluded that local economic development should support the creation and maintenance of attractive places of social interactions across the city to foster creativity and innovation which has become even more important with the surge in homeworking due to Covid-19.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".