Set-For-Variability Predicts Responsiveness to Tier 2 Reading Interventions
Bibliographic record
Abstract
Purpose We contrasted the responsiveness to two theoretically driven Tier 2 reading interventions.Method Participants were 273 struggling readers (Mage = 7.7 years, 53.1% female) in Grades 2 and 3 in Canada. The first intervention taught phonics plus Set-for-Variability (SfV) and the second intervention taught phonics plus morphology within a pre-post-delayed posttest cluster RCT trial. We tested six theorized hypotheses concerning individual differences in reading growth using nested random-intercept cross-lagged panel analyses.Results Analyses indicated that (a) the relationship between the processes taught in our intervention (SfV and morphology) and word reading outcomes were observed only after the intervention, (b) SfV was a significant predictor of word reading outcomes at delayed posttest, and (c) SfV was reciprocally related to irregular word reading and to WIAT Word Reading from the posttest to the delayed posttest. There were no significant associations involving morphology predictors or intervention groups and few effects involving pseudowords.Conclusion Individual differences in SfV underlie post-intervention reading gains when either phonics plus SfV or phonics plus morphology is systematically taught to struggling readers. Strategic mental flexibility in word decoding as indexed by SfV serves as an important printed word acquisition tool in the opaque orthography of English following multi-componential remedial instruction.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".