Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, it has become increasingly obvious that tourism education needs serious rethinking. Surging growth in tourism arrivals and receipts, going strong since the middle of the twentieth century, spurred a proliferation of tourism programs in higher education to meet the demands of the burgeoning industry. As more complex understandings about tourism began to emerge, however, it became clear that equating the industrialization and growth of tourism with social and economic progress was far too simplistic – indeed, increases in visitation and receipts do not always reap positive benefits. Simultaneously, rapid socio-cultural and economic changes are afoot, which are rendering the future increasingly uncertain. The jobs of today are markedly different from those of yesterday, and it seems certain that those of tomorrow will be different still. Students entering the tourism sector, with its high levels of volatility and rapid globalization, are going to need different skills and understandings in order to achieve meaningful and successful professional lives. It was in recognition of this landscape of change, and the demand it drives to rethink tourism education, that the Tourism Education Futures Initiative (TEFI) was born.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it