Personality Characteristics Demanded by Employers: Analysis of Job Descriptions From University Job Boards
Bibliographic record
Abstract
Abstract Employing the keyword extraction technique of Term Frequency – Inverse Document Frequency (TFIDF) on 39,487 descriptions posted on a University Co-op and Career on-line job board from May 1, 2013 to May 1, 2020, we report the keywords associated weights for the Big Five personality traits of Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism (reversed to indicate Emotional Stability) by job category. The results indicate that one third of job descriptions do not contain any keywords: even if employers are recruiting for particular personality traits, they are not necessarily referencing them in their job descriptions. For those job descriptions with TFIDF scores, Conscientiousness has the highest TFIDF score and Emotional Stability the smallest. Regression analysis indicates that categories more technical in nature, like engineering or business, score higher for “Openness”; Accounting scores highest for “Conscientiousness”; and people-facing roles in healthcare, veterinary care, childcare/teaching and administrative services score higher on “Agreeableness” and, to some extent, “Emotional stability.”
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".