Supervision face-to-face contacts: the emergence of an intervention
Bibliographic record
Abstract
Face-to-face contacts are the cornerstone of community supervision. As community supervision in the United States and Canada emerges into a new behavioral management approach, new training curricula have emerged to conceptualize the techniques of supervision and develop the skill sets of officers. This chapter reviews five such curricula--Proactive Community Supervision (PCS) (Taxman, Shephardson, & Byrne, 2004; Taxman, 2008), Strategic Training Initiative in Community Supervision (STICS) (Bonta et al., 2011); Staff Training Aimed at Reducing Rearrest (STARR) (Robinson et al., 2012); Effective Practices in Community Supervision (EPICS) (Smith et al., 2012); and Skills for Offender Assessment and Responsivity in New Goals (SOARING2) (Maass, 2013). The comparison reveals similarities but major differences in an emphasis on the operational components for client-level change. The question remains as to which supervision intervention components are mechanisms facilitating client level change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".