Bibliographic record
Abstract
I explore the interpretation of global and regional economic integration as issues of equality of access for producers in di¤erent locations to any particular market. An industry in an individual country can be seen as becoming more globalised as producers worldwide gain more equal access to that market: if increasing access is only concentrated within a region of the world, then the market should be viewed as regionalised. This approach suggests that it is appropriate to apply economic in- equality measures (the Lorenz curve and Gini coe¢ cient and related en- tropy indices) to measuring the extent of global and regional integration. The market of an apparently open economy should be seen as regionalised (rather than globalised) if there is a high degree of equality of access be- tween producers within the region, but inequality vis-à-vis other foreign producers. In this case, the change in Gini coe¢ cient for access to a coun- try from classing together those countries within ?the region? and those outside ?the region?would be small. The third measure is the Gini co- e¢ cient comparing home and foreign producers, which corresponds most closely to standard openness measures. Overall, comparing 9 specimen economies, I ?nd that when market access for all countries is taken into account, the USA is the most glob- alised and India and China are the least globalised, with EU countries, Canada and Turkey somewhere in between. The smaller EU economies, which are very open on standard measures, should probably be viewed as regionalised rather than globalised.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".