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Record W2603714462 · doi:10.31581/jbs-25.3.3(2015)

Learning to Read Social Reality in the Light of the Revelation

2015· article· en· W2603714462 on OpenAlexvenueno aff
Haleh Arbab

Bibliographic record

VenueThe Journal of Bahá’í Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsnot available
Fundersnot available
KeywordsRevelationProsperityCivilizationField (mathematics)Social realitySociologyEpistemologyPsychologySocial sciencePolitical sciencePhilosophyLiteratureArtLaw

Abstract

fetched live from OpenAlex

Bahá’í contribution to discourses concerned with the betterment of the world and the advancement of civilization is a vast field of diverse activity, and it is by no means the intention of this article to address it in any comprehensive way. The purpose of this discussion is to make a modest offering to understanding one aspect of this endeavor—learning to read social reality in light of the Revelation of Bahá’u’lláh—as it relates to the thinking behind the programs of the Institute for Studies in Global Prosperity. The Institute works in a relatively narrow area of this field to learn systematically about enhancing the capacity of individuals and groups to participate in some of the prevalent discourses of society.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.016
Scholarly communication0.0050.008
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.129
GPT teacher head0.399
Teacher spread0.270 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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