Bibliographic record
Abstract
Activity theory, 102 Admissions, 140 application process, 140-141 challenges in culture surrounding application process, 149-151 challenges in structure of application process, 147-149 findings, 144-147 gathering and analysing information from stakeholders in sponsored admissions, 141-144 identifying and correcting handling errors, 148 insider unpublicised practices, 148-149 nineteen steps of sample application, 145-147 structural barriers to APPLICATION, 147-148 systems challenges, 147 Affiliation with schools abroad, 31 American Indonesian Exchange Foundation (AMINEF), 142 Amplification rate, 74 Applause rate, 74 Applicants, 143 Application process, 140-141 challenges in culture surrounding, 149-151 challenges in structure of, 147-149 international students' general cultural adjustment difficulties, 234-235 issues in international students' support services, 236-241 limitations and implications, 241-242 methodology, 232-234 Chinese Student Protection Act (1992), 45 Chinese students, 37, 46, 174 at Business and Economics Schools in Spain, 174-176 challenges and satisfaction, 178-180 at FECEM, 180 and professionals, 47 questionnaire to, 181-183 as spies, 46 stages of tourism development, 40 students in US, 39 in US under COVID, 40 Chinese Study Abroad Program, 39 Chung yu, 45 Club sports, 192-193 Collaboration with other HEI abroad, 28 College of Education degree program, 140 Collegiate sports, 192-193 Common method bias (CMB), 58 Communications, 26-27, 150-151 Complexity, 8 Confusion, 222-223 Conversation rate, 73 Conversion rate, 72 Cooperative typology, 104-105 Core level service, 157 Cost, 17, 23 of program, 11 Council of Higher Education (YÖK), 156 COVID-19, 247
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.776 | 0.774 |
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".