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
Purpose The purpose of this paper is to analyse the factors that make tourist shopping districts successful. Design/methodology/approach In total, 12 sets of face-to-face interviews were conducted in 7 cities on 4 continents in September and October, 2019. In total, 21 individuals participated in the interviews. Interviews were conducted in Bangkok Thailand, Singapore, Melbourne and Brisbane Australia, Ottawa Canada, New York USA (three sets of interviews) and London England (four sets of interviews). Findings The literature focusses on operational issues, while respondents highlighted higher order issues relating primarily to organisational structure, governance and funding. Research limitations/implications The study focusses primarily on English speaking jurisdictions, with the exception of Bangkok. As such, the results may not be generalisable to non-English speaking economies. Practical implications Insights into factors influencing the success of tourism retail shopping districts are highlighted, especially the role of governance and creativity. Social implications The paper indicates that local stakeholders also play a key role in the success of such districts. Originality/value This is the first comprehensive, global study of the factors that make tourism shopping districts successful.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it