Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Encouraging students to actively engage with course material is an ongoing challenge for many management educators. One common tactic is to use various technologies that allow tech-savvy Millennial Generation students to take a more active role in their learning. In this article, we describe an innovative group project that challenges students to integrate Xtranormal (text-to-video software) into a role-play exercise. This project was incorporated into undergraduate human resource management courses at two universities. Qualitative self-report student learning data were collected over the course of three semesters from 210 students. The data offer insights into why and how this project excited the students and positively affected their perceptions about designing and evaluating a training session, applying training concepts, and demonstrating other human resource management concepts. We found that providing students an opportunity to use Xtranormal resulted in a number of learning outcomes, including creative freedom that enhanced engagement with the material, greater understanding and application of concepts, and training method and design competency, among others.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it