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Record W185745425

Dispatches: Affirmative action in India

2002· article· en· W185745425 on OpenAlexvenueno aff
Sumit Ghoshal

Bibliographic record

VenueCanadian Medical Association Journal · 2002
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAffirmative actionCasteFunctional illiteracyGovernment (linguistics)PovertyLawMedicineSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0210.007
Scholarly communication0.0120.005
Open science0.0030.011
Research integrity0.0180.022
Insufficient payload (model declined to judge)0.0670.015

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.

Opus teacher head0.060
GPT teacher head0.391
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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