Bibliographic record
Abstract
A New Geo-Economy PART ONE: PATTERNS OF GLOBAL SHIFT Introduction A Brief Historical Perspective The Global Economic Map Trends in Production, Trade and Investment PART TWO: PROCESSES OF GLOBAL SHIFT Introduction Traditional Explanations and the Need for a New Approach 'The State Is Dead ... Long Live the State' National Variations in Policy Stance Technology The 'Great Growling Engine of Change' Transnational Corporations The Primary 'Movers and Shapers' of the Global Economy 'Webs of Enterprise' Transnational Corporations within Networks of Relationships Dynamics of Conflict and Collaboration 'Both Transnational Corporations and/f003 /f001States Matter' PART THREE: GLOBAL SHIFT: THE PICTURE IN DIFFERENT SECTORS Introduction The Choice of Case-Study Sectors 'Fabric-ating Fashion' The Textiles and Clothing Industries 'Wheels of Change' The Automobile Industry 'Chips and Screens' The Electronics Industries 'Making the World Go Round' The Internationalization of Services PART FOUR: STRESSES AND STRAINS OF ADJUSTMENT TO GLOBAL SHIFT Introduction A Summary Perspective Making a Living in the Global Economy Issues of Global Governance
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".