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Record W3209696972 · doi:10.1007/978-981-16-5248-6_4

When Should We Start Doing Research and Publishing Papers?

2021· book-chapter· en· W3209696972 on OpenAlexaboutno aff
Samiran Nundy, Atul Kakar, Zulfiqar A Bhutta

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsNobel laureatePublishingLibrary scienceMedia studiesSociologyMedical educationPsychologyArt historyArtPolitical scienceMedicineLawComputer scienceLiterature

Abstract

fetched live from OpenAlex

Abstract Although basic statistics are covered during the Preventive Medicine posting in the majority of medical schools in India there is no attention paid to research. However, globally many leading universities encourage their undergraduates to do research. A classic example of this was in 1923 when Charles Best was an undergraduate at the University of Toronto, he together with Banting and Mcleod, discovered insulin. Banting shared the Nobel Prize that he was subsequently awarded with his student Best. Another Nobel laureate, Alan Hodgkin, won the Nobel Prize in 1972 for his work on nerve transmission that he had begun as an undergraduate in Cambridge, England.

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)0.058
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.185
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.009
Science and technology studies0.0050.010
Scholarly communication0.0440.036
Open science0.0050.005
Research integrity0.0100.020
Insufficient payload (model declined to judge)0.0650.099

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 head0.480
GPT teacher head0.477
Teacher spread0.003 · 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.

Study designNot applicable
DomainIncentives
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
Published2021
Admission routes1
Has abstractyes

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