Bibliographic record
Abstract
Britain’s town and city centres are in a state of crisis. All over the country, urban centres face a set of challenges caused principally by a marked decline in the business of shopping in the town centre. The evidence of this decline is there for all to see in store closures, vacant shops, mass retail redundancies and underused high streets. Recent years have been a litany of crisis and collapse in high street retailing. In 2018, more than 14,500 stores closed with the loss of more than 117,000 jobs. In 2020, more than 16,000 stores closed and over 182,000 jobs were lost. Some of the medium-term causes of these severe contractions have been building steadily for some time. The proportion of shopping done online, for example, has increased rapidly since the early 2000s, and now makes up around a quarter of all sales. Footfall in town centres has sagged year on year across the same period. Crucially, real wages and household disposable incomes in Britain have stagnated since the 2008 financial crisis – for a sector that relies upon hoovering up consumers’ spending money, this is critical.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.454 | 0.286 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".