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Record W4235509430 · doi:10.33423/jabe.v22i13.3896

Accountability Asset Recovery: A Leadership and Sustainability Initiative

2020· article· en· W4235509430 on OpenAlexvenueno aff
Monika Sheldon-London

Bibliographic record

VenueJournal of Applied Business and Economics · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilitySummitPublic relationsPolitical sciencePublic administrationGeneral partnershipPoliticsAgency (philosophy)Citizen journalismCompetence (human resources)Earth SummitCLARITYSociologySustainable developmentManagementEconomics

Abstract

fetched live from OpenAlex

Since Rio, also known as the United Nations Conference on Environment and Development [UNCED] held in Rio de Janeiro in June 1992 and informally referred to as “The Earth Summit,” a practical way forward has eluded leadership specifically in regard to an accountability process. High instructions have been evaded and postponed by actors on the international economic front not due to the lack of vision, clarity, or agency infrastructure, but due to a missing link in the chain at follow-through. Dialogue-based public diplomacy competence is the key to collaboration with summit leadership when instigating geo-political macro-economic initiatives correspondent to the practice of public private partnership within the vast confines of a participatory democracy. Research can find positioned in the media, via internet and electronic sources, signals, visuals, and language cues by leaders which can be utilized as building blocks in conjunction with summit level leadership directives toward public participation, by citizens. Thus this missing link is the focus of an initiative created by this writer; the topic of this research.

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.224
GPT teacher head0.348
Teacher spread0.125 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations2
Published2020
Admission routes1
Has abstractyes

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