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Record W6981486313

Elton John Aids Foundation 2015 Annual Report

2016· report· en· W6981486313 on OpenAlexfundno aff

Bibliographic record

VenueIssue Lab (Candid) · 2016
Typereport
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
FundersGoddard Space Flight CenterBanco Bilbao Vizcaya ArgentariaAlexion PharmaceuticalsUniversity of TorontoMylanElton John AIDS FoundationGeorgia State UniversityState University of New YorkHope FoundationStrykerUNICEFBloomberg PhilanthropiesGilead SciencesWells FargoCisco SystemsImpact FundBristol-Myers SquibbRockefeller FoundationNational Philanthropic TrustFondation Pour l'AuditionSteven and Alexandra Cohen FoundationRobert Mapplethorpe FoundationNew York Community TrustEli and Edythe Broad Foundation
KeywordsAnnual reportFoundation (evidence)Preliminary report
DOInot available

Abstract

fetched live from OpenAlex

Tremendous progress has been made toward this ambitious goal.But there is much more work to be done.Beating AIDS requires understanding and responding to advances in science, challenging political deadlocks, and addressing head-on the underlying causes that drive HIV infections and AIDS deaths -racism, sexism, homophobia, economic inequality, and ill-informed drug policies.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.132
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1320.091

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 head0.057
GPT teacher head0.439
Teacher spread0.382 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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