Bibliographic record
Abstract
Can geography have an influence on how much we share? Are some countries sanctuaries for Sharers? Is it possible to determine the most Sharing place in the world? If so, could it be New Zealand, with 75% of those moving there saying it’s easy to fit in? What about Norway, where the more you share, the happier you become? Canada could be a good option, as personal freedom reigns there. You could vote for equal Sweden, or perhaps it’s positive Paraguay? While Iceland boasts the smallest gender gap, maybe the strongest contender is the best country to live in – Switzerland? That said, Lithuania does offer the fastest Wi-Fi, so perhaps it pips the post as the easiest place to share? Research on sharing differentiation by place is hard to come by. One study found that Asia comes out on top as the continent with the biggest appetite for Sharing, with 78% of people willing to share their own goods. To better understand the impact of place, we met, photographed and interviewed 200 people from 30 countries. Their stories are inspiring, fascinating and diverse, but three countries stood out. They encapsulated the trends, opinions and impacts that we found worldwide: the UK, Greece and India. SHARING: IN THE UK When it comes to Sharing, despite our British reserve, we’re a pretty forthright bunch. The UK makes up a third of all Sharing activity across Europe, with 64% of us already participating in online and offline Sharing, from cars and clothes to food. 80% believe Sharing makes us happy and 83% say we’d share even more if it was easier. This propensity to share, as history tells us, is indigenous. 1761 saw ‘The Society of Weavers’ set up the first cooperative organisation of the industrial age in the Ayrshire village of Fenwick. By 1831, shared ownership models proved so popular in Britain that the first national cooperative congress was held in Manchester, paving the way for the establishment of the much-celebrated ‘Equitable Pioneers Co-operative Society’, in Rochdale. But it’s not just shared ownership that gets the Brits going. When we need to raise cash, we’re partial to a collective ‘whip round’ – a forerunner to crowdfunding perhaps?
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 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".