The digital envelope: From 'Fashion City' to digital 'Green Influencer' to 'New Greener Cities' after Covid
Bibliographic record
Abstract
This chapter explores the growing relationship between, on the one hand, digital reproduction and reality replication and, on the other, green innovation and sustainable policies after Covid-19 has become more controlled and its effects more mitigated than at present. We are particularly interested in thinking about the implications of these interventions for downward and diverted expectations for the present and further looming dystopias of ‘global tourism’. In earlier work we have paid some attention to tourism futures that exploit digital media to assist in conserving environments, both natural and cultural, from the current hiatus in the ever-burgeoning negative effects of global tourism. In tandem, we have also explored aspects of ‘green’ policies for sustainable development of future tourism opportunities. Our focus has been on urban and regional systems, which are the proximate recipients of global tourism populations and their associated travel, accommodation, subsistence and entertainment currencies, which are well in the billions and trillions respectively. In this chapter we select narratives and deduce implications from three points on a ‘digital-to-green’ analytical and policy spectrum. First, we explore the re-branding of an ‘Art City’ as a ‘Fashion City’ and think about what, if any, role ‘green-digital’ cross-fertilisation occurs after Covid-19 and the socio-spatial changes the pandemic may have wrought in the cultural milieu of Florence, Italy. We are especially interested in whether learning from similarly ‘over-touristed’ creative or cultural cities in Italy like Venice has been contemplated, practised or rejected. Similarly we analyse the success or failure of digital ‘celebrification’ of green interventions through the engagement of resident cultural icons as sustainability ‘engagers’ or ‘influencers’ through the performances of Madonna in Lisbon. Finally, we reverse the perspective somewhat in anatomising ‘green’ politics and policies for specific cities and regions that have employed in major ways digital media in addition to and with a view to turning urban ‘abandonment’ as a feature of post-Covid-19 urban conditions into sustainable ‘experience’ attractions. The greening of central Paris, Barcelona, Milan and Vancouver (now to include London’s Camden Highline and even Stockton in UK) are counter-narratives to established cases of ‘ruin porn’ that attracts tourist visits to the likes of Detroit, Chernobyl and other ‘Islands of Abandonment’.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".