The reputation-related social media competence among employees in Germany, China and the U.S.: A cross-cultural scale validation
Bibliographic record
Abstract
Whenever employees post something on social media they rely on the good judgment of other people to stop their posts ending up in the public domain, where it can hurt their employer’s reputation. With growing global social media use, cases of employees’ imprudent social media use appear to be on the rise. This paper describes the first steps in the multi-country validation of the employees’ company reputation-related social media competence (RSMC) scale. The RSMC scale contains five dimensions: technical competence, visibility awareness competence, knowledge competence, impact assessment competence, and social media communication competence. The present research assesses an abbreviated version of the RSMC scale, using data from three culturally distinct countries—Germany, China, and the United States—as part of a wider research project into the cross-cultural generalizability of the RSMC scale. Preliminary findings suggest that the RSMC short scale is valid in all three national contexts and achieves partial metric invariance.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".