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Issues and Examples of Transnational Research

2006· book-chapter· en· W4241851139 on OpenAlexaboutno aff
Tony Tripodi, Miriam Potocky-Tripodi

Bibliographic record

VenueOxford University Press eBooks · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationRegional sciencePolitical scienceReliability (semiconductor)Comparative researchQualitative researchGeographyLibrary scienceSocial scienceSociologyComputer scienceLaw

Abstract

fetched live from OpenAlex

This chapter describes five studies representing transnational research as defined in Chapter 6. The studies focus on the United States and Ukraine; Romania and the United States; New Zealand and Canada; a study of eighty countries, and a comparative study of ten countries. Reasons for classifying the studies as transnational are provided. Issues pertinent to transnational research, as derived from critical analysis of the studies are discussed including translation, response rates, missing data, sampling, surveys, qualitative research, longitudinal research, reliability and validity, standardization, research design, and secondary data analysis.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0090.018
Scholarly communication0.0090.015
Open science0.0020.009
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0100.002

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 head0.336
GPT teacher head0.464
Teacher spread0.128 · 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
DomainMethods
GenreMethods

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
Published2006
Admission routes1
Has abstractyes

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