Prestige, Parallel or Predatory — Pricing Strategies amongst Taiwanese Universities
Bibliographic record
Abstract
Many countries throughout the world are actively striving to expand higher education, but the public funding available for higher education is not commensurate with the rapidly increasing number of colleges and universities, bringing about serious financial deficits and compromises in quality at many schools. In such an environment, tuition is one of the most important sources of funding, such that the financial health of many schools largely depends on the success of their tuition pricing strategy. This research is thus aimed to determine how much tuition the potential students were willing to pay to attend different schools, and to calculate the optimal rate of tuition each school should charge with respect to prestige pricing, parallel pricing, and predatory pricing. Most administrative personnel at academic institutions, when formulating tuition rates, do no more research than checking to see what other schools are charging, without giving much consideration to differences in quality. The results of this research can be used as a reference by the case schools it examines to select a suitable pricing strategy in a rapidly shrinking market. More importantly, it is hoped that other universities will be able to make use of this simple, quality-based pricing methodology developed in this research.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".