National Innovation: Mobilizing Nations for the 21st Century Economy
Bibliographic record
Abstract
Abstract Rarely have international economic fundamentals undergone such rapid and dramatic transformation, In a period of little more than two decades, China emerged from Communist isolation to become one of the most dynamic economies in the world- With the World Trade Organization establishing itself as one of the most important international associations, freer trade and the development of a globally integrated economy became the key development of the last years of the 20th century. Outsourcing emerged as one of the most important commercial processes of a generation, as manufacturers relocated factory operations to low-cost countries, particularly China, and in one of the most unexpected shifts in many generations, service companies capitalized on liberalized laws and communications technologies to move thousands of white-collar Jobs from industrial nations to emerging economies. Casting a pall over the otherwise remarkable and positive international changes were the unrelenting poverty of sub-Saharan Africa and large portions of Central and South America, religiously based conflicts that destabilized the Middle East and launched an era of global terrorism, the depressing decay of Russia, looming international oil and gas shortages and skyrocketing prices, falling birth rates in the industrial world and ominous environmental dangers associated with global warming.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".