Bibliographic record
Abstract
The topic of this paper is national culture and its impact on workplace incivility. Geert Hofstede, an expert on cross-cultural dimension and management has defined culture as “The collective programming of the mind that distinguishes the members of one group or category of people from others”. With this being said, this paper will allow for greater understanding and insight as to how national culture impacts organizations, their employees, and most importantly workplace incivility. Workplace incivility is a low-intensity deviant behavior that is counterproductive in its nature, in simple terms, incivility is described by everyday uncivil acts in the workplace. We will begin by diving into workplace incivility and its characteristics followed by national culture and the role that it plays into incivility in the workplace. Furthermore, a survey on incivility was conducted between employees in Croatia and Canada and it was subsequently used along with data collected by Geert Hofstede on these two countries. Through this, it can be said that national culture impacts the amount of experienced and perpetrated incivility at work. Additionally, the study aimed to explain the correlation between Hofstede’s model of national culture and demographics such as gender, age, amount of work experience, job position to the amount of incivility experienced/perpetrated at work. However, no strong correlation was found and as such, it was concluded that Hofstede’s model of national culture is not applicable at an individual and organizational level.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".