Bibliographic record
Abstract
Over the past century, NGOs have been rapidly growing in numbers have become increasingly involved in such health crises as HIV/Aids and Ebola around the world. Many organizations have also been founded to recognize and support oppressed groups in certain countries, one of the most important of these being women. It is undeniable that women of developing nations have been greatly affected by the rise of NGOs, and the ensuing phenomenon of NGO-isation, from increased opportunities for activism, to unsustainable dependencies on nutritional supplements,. This article presents a background of both NGOs and the plight of women in developing nations, as well as attempting to draw a relationship between these two stakeholders in our global society. This article also presents evidence to support the hypotheses that NGOs allow women to become more politically and socially active through government-neutral involvement, but also hinder their health and job prospects by failing to employ local workers and using short-term solutions instead of sustainable ones. Major analysis is conducted on these topics and attempts to determine the correlation between NGOs and their involvement with women in impoverished communities. The article concludes with final comments from the author about their overall experience and thoughts on the issue.Au cours du précédent siècle, les ONG sont rapidement augmentés en nombre et en implication dans plusieurs pays en développement en conséquence de plusieurs crises de santé telles que VIH / SIDA et Ebola. Plusieurs organisations ont aussi été créés pour donner reconnaissance à certaines groupes dans des pays oppressifs, un des plus importants parmi ces groupes étant les femmes. Il est indéniable que les femmes des pays en développement ont été aidés considérablement par la montée des ONG et le phénomène qui s'ensuit d'ONG-isation. Cet article présente un contexte d'à la fois les ONG et la situation des femmes dans les pays en développement et décrit une proposition de recherche pour tenter de déterminer la relation entre ces deux très importantes parties intéressées dans notre société globale. Cette proposition de recherche décrit ses objectives, buts et hypothèses qui concernent divers aspects de la vie d'une femme et ensuite ça décrit pourquoi ceci est un problème important et comment les données vont être obtenues. L'article conclut avec des commentaires finales de l'auteur à propos de leur expérience générale et leurs pensées concernant le problème.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".