Are High-Probability Request Sequences as Low an Intensity Intervention as Portrayed?
Bibliographic record
Abstract
High probability request (high-p) sequences, based on the momentum of behavior principle, have been an effective intervention for improving compliance and work completion for students who display challenging behaviors. They have been portrayed as a low-intensity intervention because of being perceived as simple, clear, and easy for any teacher to implement as compared to developing a token economy, behavioral contract, or conducting a functional behavioral assessment which are intensive and require expertise in applied behavior analysis. However, high-p request sequences may not be as low-intensity as has been depicted. There are several subtleties for implementing them effectively that teachers would not automatically understand. Also, an examination of the research may raise concerns how well this intervention translates into practice. The purpose of this articles is to provide foundational and theoretical information that is often overlooked when researching and implementing high-p request sequences, describe different techniques for building behavioral momentum, address issues translating research into practice, discuss problems in following published implementation steps, and offering an alternative approach for engendering student compliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".