Bibliographic record
Abstract
This essay examines economic inequality and poverty among Asian Americans and Pacific Islanders (AAPIs) and their participation in safety-net programs. Income and wealth disparities have increased dramatically over the last few decades, reaching levels not seen since the 1920s. One of the consequences has been an inability to ameliorate poverty, particularly among children. While Asian Americans have been depicted as outperforming all other racial groups, they have not surpassed non-Hispanic whites after accounting for regional differences in the cost of living. Moreover, a relatively large proportion of AAPIs is at the bottom end of the economic ladder. Many impoverished AAPIs rely on antipoverty programs to survive, but most still struggle because of a frayed safety net. Most experts believe that inequality will persist or worsen; consequently, it is likely that the absolute number of poor AAPIs will grow over the next quarter century. Addressing the problems of societal inequality and AAPI poverty will require political action to rectify underlying structural and institutional flaws, and a renewed commitment to ensuring all have a decent standard of living.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".