K-Net Project. Preservación de los pueblos indígenas a través de las nuevas tecnologías
Bibliographic record
Abstract
espanolEn diferentes paises se han desarrollado proyectos con la finalidad de llevar las nuevas tecnologias de la informacion y comunicacion (TIC) a todos los espacios y a todas las personas, incluso a aquellos lugares que por su situacion geografica resulta aun mas dificil. Este es el caso de las comunidades indigenas o primeras naciones.En este trabajo presento una aproximacion, desde mi vivencia, a las acciones que el proyecto Kuh-ke-nah-Net (K-Net) ha llevado a cabo en las comunidades indigenas del norte de Ontario, Canada para llevar apoyo en temas de salud, educacion, justicia, economia, a traves del uso de las TIC. EnglishDifferent countries have developed projects in order to bring sew information and communication technologies (ICTs) to all areas and all people, even those place that, by their geographical location, it is more difficult. This in the case of indigenous communities or First Nations.This paper presents an approach, from my experience, to the actions that the project Kuh-ke-nah-Net (K-Net) has brought in indigenous communities in northern Ontario, Canada, which has as an objective the support on health issues, education, justice, economy, through the use of ICTs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".