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, unpacking Brexit: evidence from Regional Trade Data. The debate on BREXIT has now gathered considerable momentum, spurred on by the decision taken to seek Parliamentary approval for triggering Article 50. Discussions are, however, still at quite a high level of aggregation. Will it be a “hard” or a “soft” exit? In reality, it is unlikely to be a simple choice between two extremes. The debate will inevitably deepen as policymakers look more closely at the detail. Brexit poses questions that have not previously been asked with such urgency. What are, for instance, the patterns of regional trade across the UK with regard to flows of goods and services from other EU states and countries beyond? Which regions and localities would be most affected by leaving the single market with consequences for import and export costs? The short answer to this is that we do not know the full picture. The data is partial and much of it still classed as experimental. HM Revenue and Customs, however, publish quarterly Regional Trade Statistics for goods and materials that at least give an indication of the regional dimension of international trade links affecting manufacturing. The most recent issue (published on December 6, 2016) relates to the third quarter of 2016. Overall, this data shows that in the year to September 2016, the overall value of UK exports of goods increased by 0.2 per cent compared to the same period last year. This seems consistent with trends in other indicators that suggest “business as usual”, at least for now.
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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 | 0.111 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".