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Record W3204884275

Stages of Learning Transport Terms in English on the Basis of Modern Technologies

2021· article· en· W3204884275 on OpenAlexaboutno aff
Mustaeva Guldora Salaxiddinovna, Qurbanova Muxabbat Mamadjanovna, Saydivalieva Barno Saidbaxromovna

Bibliographic record

VenueTurkish Online Journal of Qualitative Inquiry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Innovation and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSerfdomIndustrial RevolutionEconomic historyDivision of labourQuarter (Canadian coin)Late 19th centuryHistoryEconomyWorld historyHumanitiesPolitical sciencePeriod (music)EconomicsAncient historyArtLawArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The development of technology and technology of material production, the processes of division of labor, its specialization and cooperation, and ultimately, the entire world economy, culture and language, as a means of human communication, storage and transmission of information, was greatly influenced by three industrial revolutions, which covered more than two hundred years of history. The first, the Industrial Revolution ( PR ) (from the last third of the 18th century to the last third of the 19th century), affected a limited number of countries: England (from the last third of the 18th century to the first quarter of the 19th century ), France (after the 1789 1794), Germany (from the 40s of the XIX century), the USA (after the civil war of 1861-1865), Russia (only after the abolition of serfdom in 1861), and Japan only by the end of the XIX century. For this reason, the Industrial Revolution was accompanied by the simultaneous expansion of the English technical language into other languages.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.008
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.162
GPT teacher head0.467
Teacher spread0.305 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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Same venueTurkish Online Journal of Qualitative InquirySame topicEducation, Innovation and Language StudiesFrench-language works237,207