Bibliographic record
Abstract
California is at the cutting edge of technological change, demographic transformation, and international engagement. It has the country's largest population, and is its biggest producer of agricultural and manufactured goods, its main exporter and importer, and a leading center for higher education, research, the media, and philanthropy. Its population is the most international; more than a quarter of the state's residents were born in another country. But habits of thought and structures date from the mid-twentieth century, when California was turned inward. California today lacks ideas, institutions, and policies commensurate with its global stakes and clout. Global California addresses an important subject: how the citizens of a state with the dimensions and power of a nation are affected by international trends, and what they can do to identify and promote their own interests in a rapidly changing world. In this fresh, well-informed, and balanced analysis, Abraham Lowenthal deals with numerous thorny issues—from globalization, trade, and infrastructure to immigration, environmental pollution, climate change, and California's ties with neighboring Mexico and the dynamic Asian economies. A recognized authority on foreign affairs, Lowenthal argues that the real choices are not whether to cheer globalization or condemn it. Rather, Californians need to think strategically and act effectively to gain as much as possible from international engagement while managing its risks and costs. They need to build "cosmopolitan capacity" to understand and respond to global challenges and opportunities. Too much is at stake for California—its citizens, government, firms and non-governmental organizations—to leave thinking and acting on international affairs to the federal government and to East Coast think tank experts. This volume shows Californians how to succeed in an ever more interconnected world.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.569 | 0.260 |
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".