Fidelity First in Middle School Reading Programs
Bibliographic record
Abstract
Middle school reading scores throughout the state of California have been predominantly less than average in recent years. A school located within this region has struggled to raise reading scores. An unknown problem existed that stemmed from the implementation of the school’s reading program. The purpose of this investigation was to (a) determine the level of fidelity to the reading program, (b) understand the teachers’ perceptions of the reading program, and (c) understand the structure of the reading program. The theory of andragogy guided this qualitative case study. Six teachers from a local school participated in the investigation. The teachers were purposely selected to take part in semi-structured interviews. Two sets of data were gathered for this investigation: (a) results from semi-structured interviews, and (b) publicly available reading data. The data were coded, and emerging themes were outlined. Six themes emerged to understand the overall process of the reading program. The results of the study pointed to the need for a more focused and sustained reading program. Another finding from the investigation was that teachers need year-around training in implementation fidelity. Another finding was that the reading program’s structure can benefit from the 5 constructs that make up implementation fidelity. The implications of this study may affect positive social change by providing teachers with sustained training and support to be effective reading development facilitators. Well-trained teachers have a profound effect on their students and providing teachers a platform to guide these students toward a literate world can make a positive social change in their communities.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".