Educación y pobreza: factores de cambio social
Bibliographic record
Abstract
This research article explores the factors related to social change through education in contexts of impoverished and marginalized youth in Veracruz. For this purpose, it recompiles data from the collection of information gathered from a project on young people through a qualitative approach carried out in medium-sized cities of that state, in which the political participation of young people was the main focus, but the educational issue was very important to understand the youth trajectories.\nOur article stems from the research “Promoting spaces for civic participation, inclusion and the reduction of Violence” sponsored by the IDRC (International Development Research Center of Canada). Which focused on the social inclusion and citizenship of young people in environments of violence, vulnerability and exclusion in the state of Veracruz. It was carried out in collaboration with the Universidad Veracruzana, the Municipal Services Center (CESEM) and the Movement to Support Working and Street Children (MATRACA-AC) during 2017 to 2020. The survey of the project from which we recovered data For this work, the research consisted of 20 focus groups, 46 interviews with young people and 14 with teachers in Xalapa, Coatzacoalcos, Veracruz and Poza Rica, cities in the State of Veracruz. The data indicates that, at minimum, there are five factors that constrain youth educational projects: family income, aspirations, social context, social networks and teacher support.\nThese elements of contingent combination determine the youth trajectories identified in the school field and can explain both permanence and dropout from school in these cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".