Bibliographic record
Abstract
Over the course of 2018 - 2019, interactive experience design company, (ix)plore Lab, created three simulation-based learning programs for the Travis County Reentry Employment Services (RES) training in Austin, Texas. Participants, most of whom were from organizations that assist formerly incarcerated individuals during the reentry process, attended workshops focused on client-based practices. Specifically, they learned techniques for better conducting informal assessments and motivational interviewing methods they could utilize in their work. Each workshop culminated in an interview with a fictional client, played by an actor. The team wished to improve upon existing live simulation models in several ways. First, by creating a method to provide immediate feedback to learners. Second, by increasing the simulations adaptability so it could adjust to each individual learner. Lastly, the simulation needed to have a high degree of emotional fidelity. To achieve these, (ix)plore Lab designed the programs by integrating techniques from interactive theater performance with existing simulation practices to effectively target specific skills for development. This article provides an overview of the techniques involved and adjustments made to focus on soft skill development, and documents three simulation-based learning programs that took place over the course of 2018 - 2019, highlighting developments made with each iteration. The training program documented in this paper was created without monetary compensation. In lieu of payment, the organizers of RES allowed (ix)plore Lab to collect feedback from learners and to use the training as a laboratory for workshopping this simulation-based learning model.
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 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 | 0.003 | 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) | 0.001 | 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".