Ranking Candidates: An Experiential Exercise in Personnel Selection
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
Personnel selection is a key topic in Human Resource Management (HRM) courses. Many selection exercises focus on management situations that are unfamiliar to students who are taking introductory HRM courses. In contrast, this exercise introduces students to the pre-interview steps in the personnel selection process by asking them to determine the knowledge, skills, and abilities of potential adjunct instructors for a future offering of an HRM course. Groups of students act as management teams to determine the suitability of four applicants. Tasks include determining desirable qualifications, and then developing and ranking selection criteria based on the job posting. Subsequently, each group reviews the resumes of the four applicants and ranks them based on their selection criteria. A plenary discussion follows, during which students compare their choices and provide their rationale for their rankings. A discussion based on key questions concludes the activity. The exercise may be conducted in class or online.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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