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Record W2008441554 · doi:10.7202/004163ar

Problems of Mining Terminology in India

2002· article· en· W2008441554 on OpenAlexvenueno aff
Rajendra Kumar Singh

Bibliographic record

VenueMeta Journal des traducteurs · 2002
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTerminologySyllabusHindiGovernment (linguistics)CommissionComputer scienceSubject (documents)Scientific terminologyLinguisticsArtificial intelligenceLibrary sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

In 1961 the Government of India appointed a Standing Commission for Scientific and Technical Terminology to formulate principles for evolution of terminology and preparation of standard textbooks in Hindi and other Indian Languages. The Commission published several glossaries of Engineering terminology, but none of mining engineering. From 1943 to 1954, Professor Raghu Vira, the noted Indian linguist, also published quite a few special dictionaries, and for the first time collected and compiled some mining terms in his Comprehensive English-Hindi Dictionary (1981). Though these indicate a first positive move toward collecting, processing and disseminating specialised vocabularies, their authors' principles and methods of developing terminologies vary. For want of a standard terminology of mining in Hindi and a lack of understanding of terminological concepts and their interrelationships, no textbook of mining could be written in or translated into Indian languages. It is also realised that translation of mining literature should be done by mining engineer translators who understand the systems of concepts, systems of terms and principles of translation. For a wider dissemination of scientific knowledge and technical skills, development of terminologies in Indian languages on internationally accepted sound terminological principles is necessary, even though presently subject specialists communicate in English. With the present government formulating programmes to use on a large scale the new communication technology in our school system, teaching of terminology within the framework of ESP syllabus at undergraduate level is also suggested.

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)0.038
metaresearch head score (Gemma)0.113
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.113
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0290.050
Science and technology studies0.0150.019
Scholarly communication0.0220.020
Open science0.0100.019
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.002

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 head0.073
GPT teacher head0.241
Teacher spread0.168 · 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
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

Citations1
Published2002
Admission routes1
Has abstractyes

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