Asian Americans and Pacific Islanders Facts, Not Fiction: Setting the Record Straight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This report is founded on the simple premise that educational policies and practices must be based on fact, not fiction, if they are to be of value to teachers, students, parents, and society as a whole. The report focuses on three pervasive and core fictions about the Asian American and Pacific Islander community, which are examined in the context of empirical data. In addition, three issues of emerging importance are presented to highlight new conversations that are surfacing among educators on college campuses. Facts, Not Fiction: Setting the Record Straight serves as a source of consolidated information that will be valuable to anyone interested in advocating for fair and better educational practices. In particular, through the frame of advocacy and social justice, the report provides educators, policymakers, students and their families, and advocates with accurate and up-to-date information, enabling them to critically examine the extent to which their schools meet the demands of an increasingly competitive and global environment and advance the principles of equality and justice.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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 it