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Record W2004997499 · doi:10.1080/00210862.2012.703486

Iran's “Twenty-Year Vision Document”: An Outlook on Science and Technology

2012· article· en· W2004997499 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIranian Studies · 2012
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMcGill University
Fundersnot available
KeywordsRatificationRanking (information retrieval)Plan (archaeology)Investment (military)Regional sciencePolitical scienceEconomic growthGeographyComputer scienceEconomicsPoliticsArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

In 2005, Iran outlined its goals in economic, science and technology for the next twenty years, Sanad-e- Cheshm Andaz-e Bist Saleh (The Twenty-Year Vision Document), a working plan to raise the country's ranking to that of the first in the region. This article aims to map Iran's scientific and technological performance over five years since the ratification of the plan. Three main areas of science and technology—the percentage of GDP invested in knowledge, scientific performance and technological performance—were used to compare Iran's scientific output with a set of regional countries. The study revealed that Iran's investment in science to inspire technology (the linear model) has been able to nourish scientific performance in the form of rising publication, whereas the neighboring countries followed a more diversified pathway and inspired science from technological advances. Thus, the number of countries in the region capable of competing with and even outstripping Iran in terms of technological and hence scientific performance has increased.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0440.117
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.543
GPT teacher head0.606
Teacher spread0.063 · 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