Bibliographic record
Abstract
The rapid rate of technological change and the rapidly growing number of institutions now embarking on Internet-based delivery means that more institutions are involved in distance education than at any other time in history. As institutions throughout the world increasingly offer courses via the Internet, there will emerge a global higher education economy in which institutions will face global competition for students, especially those involved in continuing professional education and lifelong learning. The emergence of the global higher education economy could well act as a catalyst for overcoming the institutional inertia that typifies the organisational culture of many universities. This transition from the Industrial to the Information Age was encapsulated by Dolence and Norris (1995), who argued that to survive organisations would need to change from rigid, formula driven entities to organisations that were "fast, flexible, and fluid" (p. 31) -- adjectives not typically used to describe the salient features of universities! This case study outlines the response of a well-established dual mode institution, The University of Southern Queensland (USQ), to the "gales of creative destruction" (Schumpeter, 1950, p. 84) that currently beset higher education institutions throughout the world.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.271 | 0.079 |
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".