The Cultural Values of Public managers of Bahia: a comparative analysis between Bahia and Developed and Developing Countries
Bibliographic record
Abstract
This article evaluates the perception of the public managers of the State of Bahia with regard to the cultural values and it compares these perceptions with managers of Canada, France, Marroco, Mexico, Chile, Guinea and Cameroon. It is assumed that the cultural values influence the behavior of individuals and the way they perceive their roles. It is important to determine how they perform their activities (Schein, 2004; Thevenet, 1993; Hofstede, 2003). It is also assumed that the comprehension of culture is made easier by the process of comparison (Proulx, 2003; Dupuis & Davel, 2005, Dupuis, 2008). The methodology used is constituted by the use of a closed questionnaire to the public managers of the State of Bahia. As to the compared analysis, this work uses the data published by Proulx ( 2008). The handling of the data was through descriptive statistics. The results indicate that the managers of Bahia give priority to the administrative rules and the relation between the public position and the world of politics. The results also indicate that in some aspects, the public administration of Bahia has evolved towards the profile described by the new managerial public administration. There exist similarities in some aspects between the managers of Bahia and the managers of countries such as Canada, France and developing countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".