Bibliographic record
Abstract
Asynchronous capstone courses often lack clear opportunities for students to gain confidence in applying skills they’ve learned throughout their program of study. To counteract this, I developed an HR capstone simulation course. Through the experience, I learned the power of low-tech role play to reinvigorate the online capstone experience. To accomplish this, the course author and I created Jade Stone, a fictional home décor and accessories company and the site of an eight-week simulation. Students entered the simulation as Jade Stone’s new HR manager and were required to solve increasingly complex issues. I leveraged my design background to make branding assets and emails, further immersing students. The course author and I developed engaging avatar videos, discussions, and assignments to prepare students for HR careers by integrating academic knowledge with real-world applications. Student feedback showed the continuity of story arc and practical application made this coursework highly engaging. Developing the simulation was intensely collaborative, requiring meticulous planning to seamlessly embed content without disrupting the fictional world. Though the course took much creative planning, it resulted in a delightful capstone experience that allowed students to gain confidence as they imagined themselves as HR managers.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".