Bibliographic record
Abstract
This chapter explores the various facets of screenPLAY, an interactive video intervention for at-risk teens, which presents social skills in a medium that is both familiar and motivating to this age group. The chapter begins with a discussion of the pedagogical ideas that motivated the creation of screen- PLAY, from the necessity to move away from a skill-driven to a content-driven social-skill intervention, to promoting learning from experience, and then to the importance of clarifying learning objectives. In addition to the adoption of a constructivist perspective, a case is made for including cognitive and linguistic concomitants with social skill acquisition. A description is provided of how these additional two variables relate to behavior and the way they are integrated in the structure of the intervention. A cognitive skill is embedded in each of the eleven templates used to present content. Video clips displaying vignettes employing student actors are analyzed in a context that requires users to record their responses, thoughts, and observations in audio or text files that are uploaded to be accessed later by other users. The anonymity of both users and actors is protected, first, by the provision of an avatar to represent the user, and then, by having the video clips transformed into a comic book look. The technical details of the construction of this digital platform are provided, as well as a dialectic analyzing how the obstacles, encountered along the way, ultimately contributed to the overall innovative functionality. Future directions are examined in the context of screenPLAY’s modular structure that allows the addition of content and functionality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.165 | 0.037 |
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".