The use of rank order data in segmentation analysis: A case study of Bruce Country, Ontario, Canada
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
A topic of interest to many marketers and researchers is how potential visitors select a destination. Although numerous models have been developed, this line of research is still under-developed. This article illustrates an innovative approach to decision-based segmentation based on Unidimensional Sequence Alignment (USA). This tool was developed in molecular biology to compare genetic structures among different organisms. The basic logic behind USA is that the sequence of a series of entities/events reveals essential information about the identity of the sample being studied. In the case of this study, the sequence refers to the order in which potential visitors considered various criteria regarding potential destinations. Similar sequences of decisions indicate similar segments of potential visitors. Using data from a web-based survey of people who had requested tourism information from Bruce County, Ontario, Canada, the study demonstrates the potential of USA as well as areas for further development.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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