The use of technology and the internet in the adaptive business and marketing strategies for the sustainability of small to medium sized travel agencies
Bibliographic record
Abstract
Technology and the Internet have brought on monumental changes in how we live, learn, communicate, shop and travel. Businesses that have been around for decades have evaporated as a result of the expansion and growth of the Internet. In the travel and tourism industry, travel agencies are seen as middlemen, and it makes sense that efficiencies could be gained by removing them. The Internet has enabled the supplier to reach the consumer directly, and the consumer can go directly to the source. This notion has been brought up many times over the past ten to fifteen years, and yet travel agencies are still around. Various studies show that there still exists value for agencies. Customer service, custom products, education and consultation are all resources and capabilities valued in travel agencies. If a travel agency can differentiate itself through its value proposition and value chain, provide custom products and services to its clients, align itself strategically with other industry players, and brand and market itself through carefully evaluated and selected channels, it will not only survive in the industry but can thrive as well. --Leaf ii.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".