Caracterización de las empresas sin registro del municipio de fusagasugá para el año 2022
Bibliographic record
Abstract
Como objetivo general tenemos caracterizar las estrategias empresariales de las organizaciones sin registro del municipio de fusagasugá para el año 2022, la población está conformada de establecimientos sin registro ahora veamos que un modelo o ejemplar de estos que consolidan y sedimentan parte del revestimiento rentable y del trabajo son las microempresas, habría que mencionar que según reportes de informalidad para establecer es que Muestra la tasa del departamento nacional de estadística indica que índice de informalidad en el trimestre fue 44,3%1 lo cual establece para la vigencia de 2022 en el municipio de Fusagasugá 3361 establecimientos sin registro. El 23% de las empresas con varios dueños tiene como estrategia principal hoy en día es fundamentalmente afinar el producto/servicio para que sea atractivo para los clientes As a general objective we have to characterize the companies without registration of the municipality of fusagasugá for the year 2022, the population is made up of establishments without registration now let's see that a model or example of these that consolidate and sediment part of the profitable coating and work are microenterprises, it should be mentioned that according to reports of Sample The rate of the national department of statistics indicates that the informality index in the quarter was 44.3%, which establishes for the validity of 2022 in the municipality of Fusagasugá 3361 establishments without registration. 23% of companies with multiple owners have as their main strategy today is fundamentally to fine-tune my product/service to make it attractive to customers
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".