Educational Policy in the Post-racial Era: Federal Influence on Local Educational Policy in Hawaii
Bibliographic record
Abstract
On March 27, 2008, Newsweek ran an article titled, “Obama’s Postracial Test: How will the Democratic Candidate Deal with Potentially Divisive Ballot Initiatives Calling for an End to Affirmative Action?” And, the August 6, 2008 issue of the New York Times Magazine featured an article titled, “Is Obama the End of Black Politics?” Since then, writers from the right and left have raised and challenged the idea that the election of Barack Obama somehow signals a new, post-racial era and presidency. But what does this mean for Hawaii? With its unique racial diversity and its connection to Obama, might Hawaii somehow represent the first post-racial state? And, does this mean anything for the way education is run in that state? In addressing these questions, this paper looks carefully at the Obama Administration’s recent education initiative called the Race to the Top Fund and examines its implications for education in Hawaii.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".