Education in the 21st century : meeting the challenges of a changing world
Bibliographic record
Abstract
During the twentieth century, the United States was a world leader in raising the educational attainment of its population. This important achievement contributed to national productivity growth and extended economic opportunity to formerly disadvantaged groups in society. Now, at the beginning of the twenty-first century, U.S. institutions of higher learning retain an excellent reputation for quality. Less confidence exists, however, in the educational system's ability to meet broad economic and social objectives adequately. This uncertainty stems in part from the shifting global economy and the evolving nature of employment. These doubts also reflect the legacy of widening income inequality over the past quarter century. These concerns have sparked both federal and state legislation to reform elementary and secondary schooling. ; In June 2002, the Federal Reserve Bank of Boston held its 47th annual conference, titled \\"Education in the 21st Century: Meeting the Challenges of a Changing World.\\" This conference brought together educators, researchers, economists, policymakers, and individuals from the private sector to analyze current institutional and financial arrangements in the area of education. In this article, conference organizer Yolanda Kodrzycki provides an overview of the conference's themes and areas of consensus, as well as a synopsis of each of the formal presentations.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".