Tell Me About Your Job. . .: An Experiential and Relational Job Analysis Exercise
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
Without updated job descriptions, workers are likely to lack role clarity and the effectiveness of important human resource management (HRM) functions will be hindered. Yet, organizations frequently scrimp on or altogether skip the process necessary for producing those descriptions: job analysis. Many introductory HRM students similarly identify job analysis as the most opaque and least interesting topic they learn about. The job analysis interview exercise (JAIE) addresses these pedagogical challenges. It involves conducting a job analysis interview with a university employee who is working in a job related to students’ occupational field of interest. They use this information to produce a job description and critical assessment of the job’s design, then receive feedback on their process and output. In addition to enhancing students’ interest in and comprehension of job analysis, the JAIE contributes to the meaningfulness of interviewees’ jobs by allowing them to connect with the beneficiaries of their work.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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