Bibliographic record
Abstract
This chapter argues that the greatest asset of the Advanced Placement (AP) program over nearly seven decades has been its capacity to set and maintain lofty academic standards for high school students and to sustain those standards during times when many forces push to relax them. That is an extraordinary accomplishment, considering all that has happened in American education during this period. Academic standards of various kinds have become a big deal, a growth industry, and an endless source of controversy, especially when accompanied—as they usually are—by student tests. Advanced Placement's nongovernmental character is rare in the world of education standards, at least since 1989. That was the year that state governors and President George H. W. Bush convened in Charlottesville, Virginia, and emerged from their “summit” with an ambitious set of national education goals for the year 2000. Congress created the National Council on Education Standards and Testing to “explore the desirability and feasibility of establishing national education standards and a method to assess their attainment” and a National Education Goals Panel to monitor and report on how the country was doing in pursuit of the summit targets. Many complications, modifications, and pushbacks followed. Ultimately, the entire quarter-century sequence left many hostile both to governmental micromanagement of schooling and, especially, to anything that smacked of government-prescribed standards, curricula, and tests. With just a few exceptions and caveats, the AP program has been immune to this suspicion, rancor, and resistance.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.064 | 0.022 |
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".