Consumer and Academic Culture Convergence: the Implications of the Internet on Student Evaluations of Faculty Members and the Shaping the Rhetoric of Pedagogy
Bibliographic record
Abstract
The increasing commercialization of higher education is challenging the fundamental role of the University in today's democratic society and the consequences are grave. Increasingly, higher education is applying a customer-service approach to the student-professor relationship that is undermining effective pedagogy. Edwin Guthrie (1954) notes that the function of the University is to attempt to insure that the following generation will be more good, wise, and knowing than the present one" (p.l). Student evaluations of teaching effectiveness are often used to ensure that the function is fulfilled. Student rating websites such as Ratemyprofessor.com (RMP) offers an online community forum that exists outside the institution, where students can anonymously share evaluations of instructors with others. Students can choose instructors and courses based on the ratings. However they are selecting their professors relative to criteria that fulfills a pedagogy that is fuelled not by the drive for an enriched knowledge but by a pedagogy that is influenced by a consumer and academic culture convergence. These consumer attitudes towards higher education are spilling over into the institution and faculty members are suffering the impact. Professors need to have the freedom to motivate students to learn without having to be concerned with entertaining them. It has been argued that Universities need to re-instate their legitimacy and remind students that degrees are granted on a learning basis, not for tuition payment (Delucchi & Korgen 2002). Without a re-establishment of an academic ethic, the University could fall prisoner to the pedagogically irresponsible demands of their customers.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".