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Record W7047658749

Guidance on Protection from Sexual Exploitation and Abuse

2019· article· en· W7047658749 on OpenAlexfundno aff

Bibliographic record

VenueJMU Scholoraly Commons (James Madison University) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsSexual abuseRefugeeSexual misconductMisconductHumanitarian aidHuman rightsElement (criminal law)Task (project management)
DOInot available

Abstract

fetched live from OpenAlex

Sexual exploitation and abuse (SEA) by humanitarian workers are some of the most serious failures of protection and are grave violations of the responsibility of humanitarian personnel to do no harm. Whereas instances of SEA are not a new occurrence, the pervasiveness of this phenomenon was only exposed to the public eye in 2002 following allegations of SEA by aid workers against refugees and displaced women and children in West Africa. Acknowledging that the problem was global, the Inter-Agency Standing Committee (IASC) Task Force on Protection from Sexual Exploitation and Abuse (PSEA) was established in 2002 with the short-term purpose of clarifying core standards of conduct and establishing practical measures to address misconduct perpetrated by United Nations (UN) staff and affiliated humanitarian and development personnel. In 2003, the United Nations Secretary-General’s Bulletin Special measures for protection from sexual exploitation and abuse was released and became a landmark document outlining the UN’s zero-tolerance approach to SEA. One of the key elements of the bulletin was the requirement of mandatory reporting, which obliges UN staff and implementing partners to immediately communicate any concern or suspicion of SEA by colleagues through established reporting mechanisms. Meanwhile, international organisations quickly took steps in the same direction, both individually by adopting codes of conduct, reporting systems and investigation mechanisms, and collectively through coordination groups and other initiatives.4

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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