The article "An Implicit Technology of Generalization" by Trevor F. Stokes and Donald M. Baer challenges the traditional view of generalization as a passive outcome of behavior change, arguing for an active conceptualization and technology. The authors review the literature on generalization in applied behavior analysis, categorizing studies into nine headings: Train and Hope, Sequential Modification, Introduce to Natural Maintaining Contingencies, Train Sufficient Exemplars, Train Loosely, Use Indiscriminable Contingencies, Program Common Stimuli, Mediate Generalization, and Train "To Generalize." They highlight the importance of programming generalization explicitly rather than passively expecting it. The review emphasizes the need for more comprehensive and systematic research on generalization, particularly in areas such as training sufficient exemplars, using loose teaching techniques, and making reinforcement contingencies indiscriminable. The authors also discuss the potential of using common stimuli and mediators like language to enhance generalization. Overall, the article calls for a more robust and explicit approach to programming generalization in applied behavior analysis.The article "An Implicit Technology of Generalization" by Trevor F. Stokes and Donald M. Baer challenges the traditional view of generalization as a passive outcome of behavior change, arguing for an active conceptualization and technology. The authors review the literature on generalization in applied behavior analysis, categorizing studies into nine headings: Train and Hope, Sequential Modification, Introduce to Natural Maintaining Contingencies, Train Sufficient Exemplars, Train Loosely, Use Indiscriminable Contingencies, Program Common Stimuli, Mediate Generalization, and Train "To Generalize." They highlight the importance of programming generalization explicitly rather than passively expecting it. The review emphasizes the need for more comprehensive and systematic research on generalization, particularly in areas such as training sufficient exemplars, using loose teaching techniques, and making reinforcement contingencies indiscriminable. The authors also discuss the potential of using common stimuli and mediators like language to enhance generalization. Overall, the article calls for a more robust and explicit approach to programming generalization in applied behavior analysis.