Disciplinary Culture and Effective Teaching: A Cultural Anthropological Study
Bibliographic record
Abstract
During the last couple of decades, many researchers have been trying to explicate "effective teaching" in higher education. As a result, when one searches the term, a vast amount of papers and research reports pop up in the literature, involving lists of attributes and competencies of effective teachers. But the impressive point is that "effectiveness" has been viewed mostly from a technical vantage point and disciplinary differences have not received proportionate attention. At the same time, some sociologists of science began to view disciplines as tribes and territories who own their exclusive norms, rituals, and values. Hence, this research aims at investigating effective teaching in higher education within the framework of disciplinary culture. Methodologically, the research may be deemed as interpretive ethnography as it aims at representing emically how members of disciplinary cultures perceive and interpret effective teaching. Hence, based on Tony Becher classification of disciplines into civil and rural, two postgraduate classes were selected, namely from Pure Mathematics (involving 15 students to represent civil disciplines) and Education Studies (involving 18 students to represent rural disciplines). To collect data, the researcher deployed non-participant observation for a full semester and informal interviews were also conducted at regular intervals. The field notes and interview protocols were analyzed thematically to produce meaningful categories for results representation. As credibility was of great concern in the research, three strategies were used for this purpose namely member check, peer debriefing and prolonged engagement. Based on the interpretations, members of rural disciplines evaluate teaching as effective when it focuses on classic texts, cares about human and social issues, approaches laymen jargons, emphasizes understanding, appreciates variety of teaching strategies and learning styles, holds a critical stance towards cultural issues, and takes on a lenient approach in marking. On the other side, members of civil disciplines evaluate teaching as effective when it focuses on updated resources, is content-oriented, approaches professional terminology, emphasizes practicality, and takes on a tough stance on marking.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".