“The Real World” HF/E: Understanding the Realities of Your First Professional Job
Bibliographic record
Abstract
Welcome to the Fifteenth Annual Human Factors and Ergonomics Society Student Career Panel. While our typical career panel emphasizes what one should do before graduation to prepare for a career, it is equally important to know what to do once one starts to work on the job. Thus, this year's paper will begin with a section by Anthony Andre, emphasizing the final preparations for a new professional career as well as the job search itself. The remaining papers will discuss what to do after beginning one's career. Anshu Agarwal will discuss the first 90 days on the job, Sharnnia Artis will discuss the remainder of the first year, and Raegan Hoeft will discuss the second year. Ron Shapiro will close by focusing on the subsequent term of a given job. This paper will present tried and tested techniques as well as new ideas towards preparing for, finding, and experiencing the ideal career path and position. At the annual meeting panel discussion, panelists will provide a brief introduction and then entertain questions from the audience regarding career preparation while still in school as well as success factors on the job.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".