A Quantitative Systematic Literature Review of Combination Punishment Literature: Progress Over the Last Decade
Bibliographic record
Abstract
This review evaluated single-case experimental design research that examined challenging behavior interventions utilizing punishment elements. Thirty articles published between 2013 and 2022 met study inclusion criteria. Study quality was also assessed. Through multiple levels of analysis (e.g., descriptive statistics, non-parametric statistics), we examined (a) participant and study trends, (b) differential outcomes related to temporal reinforcement approaches (antecedent, consequent, or combined reinforcement) applied alongside punishment element(s), (c) differential outcomes related to the punishment type (negative, positive) applied alongside reinforcement, and (d) effect sizes associated with study rigor across peer-reviewed and gray literature. Our results may tentatively suggest that, for certain situations, concurrently applying punishment with antecedent reinforcement approaches may coincide with significantly larger effect sizes compared to combined temporal reinforcement approaches, while positive punishment applied concurrently with reinforcement may coincide with larger but non-significant intervention effects. Most featured articles met rigor criteria, but larger effects were seen in peer-reviewed literature.
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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 | 0.055 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".