Bibliographic record
Abstract
Leigh Kersh was born in El Paso, Texas to Moe and Rosie Kersh. Leigh attended The University of Texas at El Paso and The University of Texas at Austin before attending New York University and graduating with a degree in Physiology. While Leigh finished her degree at NYU, she worked for Federal Express in customer service and then once she graduated she became their corporate physiologist. Leigh credits her time with Federal Express for learning how to do customer service, how to run an operation, and how to train the right people for the company. Leigh grew up watching her father work in real estate and learned how to be business savvy from her father. Leigh is bi-lingual in English and Spanish and speaks Hebrew and Russian as well. Leigh left Federal Express in New York and to work in North Miami Beach Florida. While in North Miami Beach, Leigh was offered to buy a chocolate shop. Leigh recalled a chocolate shop she loved in Rockefeller Center back in New York and she seized the opportunity to purchase the shop. Leigh spent over a decade in Miami, building the chocolate shop into a massive business which she was able to sell in three months and retire. Leigh returned to El Paso on a visit and was reintroduced to a former crush and they fell in love and Leigh decided to return to El Paso permanently. She opened her own chocolate store in El Paso called Chocolat. Leigh values her customers and takes great steps to ensure that anyone who enters the shop will be able to find the right piece of chocolate for the right price. Leigh has a lot of pride in her store and only offers the best chocolate. Leigh’s advice to entrepreneurs is to be prepared to work, be organized, consistent with the product and with good customer service and to be creative so as to offer a unique product.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.252 | 0.070 |
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".