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
This research explores cross-cultural differences in skill requirements for the emerging HR analyst role. We extract data from 541 HR analyst job postings across six countries and use Python software to conduct competency-based text analysis. We also employ LDA (latent Dirichlet allocation) topic modeling technique to contrast our results. Our text analysis and dictionary creation are guided by a recently published competency model for the HR analyst role. We thus evaluate the practicality and applicability of such a competency model in a cross-cultural context. Although we find significant differences in competency weightings across our sample, these differences cannot be explained by cultural theory. Power distance and individualism predicted competency related text in a unified manner. The associations observed between uncertainty avoidance and competency-related text were counterintuitive. Our findings contribute to the scarce literature exploring cross-cultural job design and further advance the discussion on HR analyst competencies. Implications for cultural theory and job design are discussed.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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