Factors Influencing Sales in Small Restaurants at Tourism Spots in the Country
Bibliographic record
Abstract
When it comes to creating new jobs, it's often the smaller and medium-sized firms (also known as SMEs) that show the most promise ( Asquith and Weston, 1994). In the United States, most new employment were created in the late 1980s by small enterprises with fewer than twenty employees (Philips, 1993). Small and medium-sized enterprises (SMEs) had the greatest economic growth in Canada (De-Laurentiis, 1994). Almost half of Australia's workforce is employed by small and medium-sized enterprises (SMEs), and SMEs are more responsible for creating new jobs than major corporations (DFAT, 1995). Small and medium-sized enterprises (SMEs) encourage creativity and invention by using labor-intensive technology that is applicable in developing nations (McCormick, 1996).
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".