Teachers' Responses to Success for All: How Beliefs, Experiences, and Adaptations Shape Implementation
Bibliographic record
Abstract
Success for All (SFA) is a whole-school reform model that organizes resources to focus on prevention and early intervention to ensure that students succeed in reading throughout the elementary grades. In this article we use qualitative data gathered in extensive interviews and observations in two SFA schools to examine how teachers respond to SFA and how their beliefs, experiences, and programmatic adaptations influence implementation. We found that teachers fell into four distinct categories ranging from strong support for SFA to resistance. Support for the reform did not directly correlate with teachers' personal characteristics such as experience level, gender, or ethnic background. Moreover, teachers' levels of support for SFA did not necessarily predict the degree of fidelity with which they implemented it. Almost all teachers made adaptations to the program, in spite of the developers' demands to closely follow the model. Teachers supported the continued implementation of SFA because they believed it was beneficial for students. At the same time many teachers felt that the program constrained their autonomy and creativity. Implications of this study for the future successful implementation of SFA and other externally developed reform models are discussed.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".