Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Professor Frank Peck of the University of Cumbria’s Centre for Regional Economic Development writes for in-Cumbria on the big issues of the day and the economic data behind them. This month, he focuses on Cumbria’s retailing sector. December is a big month for retailers. It is fair to say that retailers continue to live through a period of extraordinary change. The challenges include, on the one hand, seismic shifts in consumer behaviour involving use of mobile technology while on the other, increasing uncertainty arising from debates surrounding Brexit and consumers’ perceptions of their future job prospects. In this context, there is much speculation about the outlook for retailing over the Christmas period and beyond. The Bank of England Monetary Policy Committee (MPC) met in early November 2017 and noted that “recent indicators of consumption had been mixed”. Retail sales volumes had fallen in September, but risen over the third quarter as a whole. Other surveys appeared to indicate a recent fall in retail volumes though consumer confidence was reported to have recovered slightly in October. The Office for National Statistics has recently released data comparing October 2017 with the same month in the previous year. Overall, sales value for this month is up by over 2.5 per cent though sales volume in slightly down, the difference largely accounted for by a rise in average store prices.
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.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it