Factores socio - economicos y demográficos determinates en la pobreza de los hogares del distrito “la esperanza” 2012
Bibliographic record
Abstract
The case of poverty is a very got a passion and around which various studies have been developed in order to have a better perspective on the causes of it in society and specifically in families, this event is also present society in our country and more specifically in reality hope district, province of Trujillo in the jurisdiction of the department of La Libertad, which according to the poverty listings INEI is within the poorest districts province because in this study raises assess what are the socioeconomic and demographic determinants of household poverty District Hope 2012?. This way to determine that are the most important factors that define or explain this phenomenon more accurately as is poverty. In terms of methodology is an applied research, data collection was conducted by the National Household Survey 2009 Fourth Quarter by the INEI and information processing was performed using the Multinomial Logit methodology. Results demonstrate that socioeconomic and demographic factors are dominant in determining the condition of poverty and within each the most prominent mention as, age, work experience, people in the household, the net income you hear a household head, access to health networks, marital status, origin, size of the company where the nuclei that exist in the home. This study also shows that 63% of families in the district are hoping within the category of poverty, extreme poor (14%) and nonextreme poor (49%) and only one (37%) is in of the non-poor category
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".