Bibliographic record
Abstract
T he Lumina Foundation’s guiding star for activity—its “Big Goal”—is that by 2025 at least 60 percent of Americans will possess high-quality postsecondary credentials with labor market value. The main reason the 60 percent mark has been adopted by most organized completion initiatives is that many other countries are approaching that percentage, and reaching that postsec-ondary credential saturation point would meaningfully improve America’s global competitiveness. 1 Countries like Canada, South Korea, and Japan, for example—all of which are poised to achieve 60 percent college degree attainment rates by 2020—have been doing a much better job credentialing their citizens than has America. 2 And, while the highest-achieving American students perform as well as their highest-achieving peers around the world, America’s aggregate achievement continues to descend in international rankings. 3 Unfortunately, if the degree change rate of 37.9 percent in 2008 for Americans aged 25–64 to 38.3 percent in 2010 remains steady, by 2025 less than 47 percent of Americans will hold two- or four-year degrees, far short of the projected 60 percent needed to fill the higher-paying American jobs that will require postsecondary education and/or training. 4 By 2011, the rate had only crept up to 38.7 percent, a gain of less than 1 percent in three years. 5 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.098 | 0.031 |
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".