Notice bibliographique
Résumé
Every year, about 10 000 young men and women enter India's 150-odd medical colleges. Through India's version of affirmative action, slightly more than 25% of them will come from families belonging to “backward castes” and tribal communities that are trying to overcome centuries of socioeconomic and cultural deprivation. Their forebears swept the streets, cleaned toilets, repaired shoes and performed other menial tasks without which a pre-industrial society simply could not function. Their reward? The sobriquet achchoot (untouchable). That single word condemned generations to poverty, illiteracy and social ostracization, with practically no hope of betterment. When India gained its independence in 1947, the new government wanted to give Harijans — members of the caste of untouchables — an opportunity to improve their lot. “Untouchability” was declared illegal in 1949, and seats were set aside for Harijans, by law, in medical colleges, other schools, state-owned companies, civil services and the military. “Today the gap [between castes] is closing down,” says Dr. Sailesh Mohite, an assistant professor of forensic medicine at Bombay's Topiwala National Medical College. Mohite, a product of the “reservation system,” says young Indians who apply through the system today are much better placed to attend medical school than they were in the past, especially economically. This year applicants in the reserved category had to score over 75% in a competitive entrance examination, while colleagues in the open category had to score 90% or higher. When Mohite began his medical training, applicants in the reserved category had to score a minimum of 55%. The real problems appear after the students are accepted. Prof. Sharadini Dahanukar, the dean at Topiwala and Mohite's boss, said some reserved-category students are simply incapable of completing the course. She said she tends to treat these students less rigorously than open-category students, “but beyond a point I cannot lower the academic standards because it is unfair to the other students.” Dr. Shreekant Sapatnekar, a former professor of preventive and social medicine, feels the main barrier to success may be language. “Most of them went to village schools where the teaching is in a local language — Marathi or Hindi. In medical college, however, everything is taught in English.” When his own students appeared for their exams, Sapatnekar asked the examiner to accept oral answers in Marathi. Dahanukar says some restricted-category students now reject offers of additional help because “they see it as another form of segregation.” There is some opposition to the reservation system, mainly because it is sometimes misused. In some cases, ambitious students have bribed government officials to get themselves certified as “backward.” Efforts to prevent this have always been thwarted because the ensuing legal cases always drag on for 10 to 20 years. “By that time,” says one official at the Directorate of Medical Education and Research, “the student has finished not just his medical studies but also undergone specialist training, and then he appeals to the court for mercy.” — Sumit Ghoshal, Bombay
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,021 | 0,007 |
| Communication savante | 0,012 | 0,005 |
| Science ouverte | 0,003 | 0,011 |
| Intégrité de la recherche | 0,018 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».