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Record W2953345401 · doi:10.20529/ijme.2019.031

Casteism in a medical college: A reminiscence

2019· article· en· W2953345401 on OpenAlexaff
Anurag Bhargava

Bibliographic record

VenueIndian Journal of Medical Ethics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Development in India
Canadian institutionsMcGill University
Fundersnot available
KeywordsReminiscenceGovernment (linguistics)Medical professionMedical educationPsychologyPolitical scienceMedicinePhilosophy

Abstract

fetched live from OpenAlex

Republicans in the US Congress, Trump has tried to overturn the Affordable Care Act (also known as Obamacare), which provides access to healthcare for uninsured Americans (12); as president he has appointed industry leaders as members of his cabinet who are doing everything in their power to reverse recent gains to reduce water and air pollution in the US; he is seeking significant reductions in programmes that provide nutritional support for poor children in the United States, and much more.Reinstitution of the Global gag rule may appear to some people as a minor issue, given the much larger number of people in the world who are affected by the array of harmful Trump administration policies.But it is more than merely a symbol of US imperialism in today's globalised world.Women constitute one half of the world's population.A US-driven policy that denies a substantial percentage of women the opportunity or, in United Nations terminology, the right to control their fertility, is nothing less than a war against vulnerable women in resource-poor countries.

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0240.032
Scholarly communication0.0140.010
Open science0.0010.008
Research integrity0.0090.033
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.372
Teacher spread0.327 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2019
Admission routes1
Has abstractyes

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