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Record W7096581380

Crimes Against Humanity The Case of the Rohingya People in Burma

2013· article· en· W7096581380 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsBurmeseGenocideCrimes against humanityBuddhismHumanityPopulationState (computer science)
DOInot available

Abstract

fetched live from OpenAlex

Burma’s Rohingya Muslims are currently subject to crimes against humanity by the Burmese government, witnessed in recent acts of murder, torture, forced displacement, imprisonment, religious persecution, and restriction of movement. Should the Burmese state fail to end such crimes against humanity, there is potential for the situation to escalate to genocide. Given its mandate “to ensure that the government of Canada does all that it can to prevent and protect civilian populations from genocide and crimes against humanity, ” the All‐Party Parliamentary Group for the Prevention of Genocide and Other Crimes Against Humanity is urged to take immediate action. i Notably, Canada should be encouraged to use regional diplomatic relations to lobby for amendments to Burma’s 1982 Citizenship Law, strengthen diplomatic relations with Burma and establish and host a truth commission to overcome recent violence. Burma’s (Myanmar) Demographics Burma’s 1 dominant ethnic group is the Burmese, accounting for 68 % of the country’s population of 55,167,330. ii A number of minority ethnic groups are also present, representing small portions of the population: Shan (9%), Karen (7%), Rakhine (4%), Chinese (3%), Indian (2%), Mon (2%) and other (5%). iii Buddhism is the predominant religion in Burma, accounting for 89 % of the population. Christians and Muslims represent ~4 % of the population, respectively. iv While Buddhism is not the state religion, the majority of the Burmese tend to follow the Theravada school of Buddhism. v Reports indicate that the

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.857

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)0.0000.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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 designQualitative
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
Published2013
Admission routes1
Has abstractyes

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