Chinese Graduates' Employment: The Impact of the Financial Crisis
Bibliographic record
Abstract
ing and expectations (or do so on their own volition), this pressure will surely be brought to bear on the colleges and universities where their children enroll.Just as cultural change is calling for "green" campuses and worksites, pressure might come to pass for a leaner, more austere academic experience, at a lesser charge to students.Institutional leaders, board members, and government educational officials face the following challenge: There is no evidence that the needs for a highly skilled and well-educated workforce are going to diminish, whatever course the economy takes toward recovery.At the same time, the conditions that provide access and opportunity to complete various forms of postsecondary education and training are languishing in this country, with essentially flat performance over the past quarter century.Furthermore, other developed countries are surpassing the United States now in percentage of the younger population with degrees and certificates, so the US first-mover status toward mass higher education has been eroded.Finding the will and the way to use the most effective educational resources is now both a moral and an economic obligation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".