The Vanguardist Good Professor in Natural and Social Sciences
Bibliographic record
Abstract
The aim of this study is to identify what features make good professors in the social and natural sciences. Are these qualities the same? Through gathering data from undergraduate business and engineering students, we searched for educator’s characteristics considered positive or ideal for the learning process. This research used primary data resources collected through online survey. Technical analysis of the content was used for interpretation of the results. Students from the social and natural sciences have similar points of view and expectations about towards their professors. Scholars believe docents should be more comprehensive and prepare students for the market, and not use the classes to expose their (students’) ignorance, lack of mastery of the subject, difficulties, nor poke fun at them. Fortunately, these behaviors are not common place, but are known to exist, and represent a display of a remarkably unprofessional, egotistical, and arrogant conduct by the instructor. Social science professors are often more communicative and show more empathy, while natural science professors are more technicians and follow the syllabus, texts and material in a more rigid manner. This research is relevant for docents to reflect on their teaching persona and about the importance of self-awareness during their Master and Doctor programs. It is equally important for educators to see themselves through their students’ eyes, so they can endlessly strengthen their own practice.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".