En-tienda: Simulate Your Success
Bibliographic record
Abstract
Small- and medium-sized businesses (SMBs) comprise a large proportion of the South and Latin American grocery retail industry, providing 67% of the region’s employment but only 1/3 of the GDP. This is disproportionately lower than small businesses in other parts of the world. Additionally, 77% of these businesses fail within only 4 years. Z-Tech’s goal is to develop innovative solutions to help the large numbers of South and Latin American SMBs in the grocery retail sector become reliably more successful. Our approach for the Winter quarter was to help expand SMBs beyond brick-and-mortar stores through Provisions, a mini convenience store embedded in local taxis. Provisions includes a self-service vending cabinet stocked with convenience items, a passenger interface that allows for customer transactions, a roof-top camera that uses computer vision to capture the seat’s current inventory and inform the driver when it’s time to restock, locks that keep the items secure until time of purchase selection, and a driver interface that allows the driver to see the current seat inventory as well as connect with the SMB network when it’s time to restock. Our final solution was En-tienda, an online education platform that simulates the dynamic, day-to-day experience of grocery retail SMB ownership. En-tienda is aimed at prospective SMB owners, to learn best practices before investing in their own brick-and-mortar store. En-tienda was developed in Unity and contains the following key features. The virtual environment enables risk and capital-investment free learning at an accelerated timescale conducive to the working adult. Key features of En-tienda include customizable store environments representative of SMBs in the region, dynamic simulation flow to capture the multi-tasking needs of SMB ownership, immersive tasks conducive to hands-on learning, tailored feedback correlating simulation performance to SMB profitability and long-term health, and embedded learning video tutorials to motivate business management knowledge.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".