Failure, The Next Generation: Why Rigorous Standards are not Sufficient to Improve Science Learning
Bibliographic record
Abstract
Although many states in the United States are adopting policies that require all students to complete college-preparatory science classes to graduate from high school, the outcomes of such policies have not always led to improved student outcomes. While there is much speculation about the cause of the dismal results, there is scant research on the process by which the policies are being implemented at the school level, especially in schools that enroll large numbers of historically non-college-bound students. To address this gap in the literature, we conducted a four-year ethnographic case study of policy implementation at one racially and socioeconomically diverse high school in Michigan. Guided by the structuration theory of Anthony Giddens, we gathered and analyzed information from administrator and science teacher interviews, observations of science classes, and relevant curriculum and policy documents. Our findings reveal the processes and rationales by which a state policy mandating three years of college-preparatory science for all students was implemented at the school. Four years after the policy was implemented there was little improvement in science outcomes. The main reason for this, we found, was the lack of correspondence between the state policy and local policies developed in response to that state policy.
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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 | 0.049 | 0.110 |
| 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 | 0.014 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".