Bibliographic record
Abstract
academic institutions that confer status 33 academic research 202 academic standards, lower, overgenerous marking 54 acceptance of situation, changed context in market 147 accountability and improved performance for government funding 11, 30, 161 accreditation 16, 92, 202 ACSI Model of Customer Satisfaction 62 adaptation to the changing world 210 adaptive learning technology 198, 217-18 admission of low admission scores, Australia 87 admission procedures, rigorous 34 advertising 179 African Americans, interviews, random sample 169 AIDA, awareness, interest, desire, action 179 airline industry, tickets with mandatory features 85 alliances and partnership academics and research/teaching consortia 162 alternative evaluation, best for needs 177 American Customer Satisfaction Index 58 firms, industries, economic sectors, national economies 62 American Marketing Association (AMA) official definition of marketing, 1948 107 American Men of Science, James McKeen Cattell 39 Apple differentiated position 220 upward growth trajectory 138 artefacts of market orientation 109, 114 assets of organization tangibles and intangibles 84 Australia allow universities to set own fees, rejected 87-8 deregulation of higher education sector 69 evolution of higher education policy 3-4 first degrees, 1856 3 first university, Sydney, 1852 small population, large distances 5 Australian dollar floating, 1983 8 Australian economy in 1980s 8 Australian government 'cap' on publicly funded places 2 Australian students with poor command of English 87 Australian Tertiary Admission Rank (ATAR) one in three chance of failing to complete 87 Australia's higher education 'behind international competitors' 13 Australasian Survey of Student Engagement (AUSSE) 19 results higher in Canada and USA 18 awareness development 179 Bacon, Sir Francis, English philosopher 'knowledge is power' 188 today, might say 'knowledge is ubiquitous' 188
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.725 | 0.597 |
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".