Bibliographic record
Abstract
This dissertation aims to identify and analyze the relations between personal values, attributes considered in the decision of choice of course, the judgment and meanings attributed and the level of satisfaction with the Master course chosen by students of Graduate Programs in Management, members of the Post-Management Program. To perform this study twenty-three courses of Academic Master in Management were analyzed, distributed in the Southeast, South, Northeast and Center-east regions of Brazil, totaling 512 students (current and graduated). With a theoretical and empirical approach, this is a descriptive study, mainly quantitative, developed from primary and secondary data. The primary data were collected by a self-administered questionnaire available in the internet for current and egresses students from the analyzed programs. The secondary data were obtained by a documental research and literature review in order to allow a better comprehension of the trajectory of Graduate Studies programs and the context of Master in Management courses in Brazil. The data analysis was made by descriptive statistical techniques and multivariate data (Cluster Analysis and Multinomial Discriminant Analysis). The proposed model of personal values influence, course attributes and judgment/meaning was analyzed and the overall hit rate in the Discriminant Analysis classification was 83.0%. The research shows that there are significant differences in student evaluations with regard to courses classified according the grouping of Master Courses (A to D). There are contradictions between the goal to search a Master Course in order to work in the academy and the egress activity. A mismatch was detected between the ideal profile of the teacher professional in Management and the current reality.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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; both teacher heads agree on what is shown here.
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".