You’re Hired! A Hiring Simulation for Sport Management Students That Incorporates the Hidden Profile Phenomenon
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
Decision making is a crucial skill for sport management students to develop. However, we are all subject to cognitive biases that may influence our decision making. Groups are often offered as a remedy to address individual cognitive biases, the maxim being that two, or more, heads are better than one. Nevertheless, group dynamics may also accentuate cognitive biases resulting in suboptimal decision making. In this teaching simulation, students are tasked with selecting the best candidate to hire for a fictional sport organization. The simulation was designed using the hidden profile condition, such that students rarely identify the optimal candidate, when the task is performed either individually or in a group. Even when the full candidate profiles are revealed, a sizeable minority is still unable to identify the best candidate. This study explains the theoretical reasoning for these occurrences and provides a detailed account of the construction of the simulation, along with details on how to implement and debrief the exercise with students.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it