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Record W2991943021

Palaces of Hope: The Anthropology by Organizations: Legal Knowledge and the UN's Ethnological Imagination

2017· article· en· W2991943021 on OpenAlexaff
Ronald Niezen

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsDignityProsperityBureaucracyHuman rightsVariety (cybernetics)SociologyCitizen journalismPolitical scienceLawEnvironmental ethicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

We are used to thinking of global institutions in terms of how they respond to security crises, human rights abuses, and development opportunities in an uncertain world, but it is less often noted that they are also among world's most significant producers of knowledge. This knowledge includes understandings of human life and its variety - something that we might recognize as anthropology, or what I have referred to as the law's legal anthropology (Niezen 2012). They prioritize improving conditions in world through powers of bureaucracy and law, and often do so by first identifying and categorizing those who are in need of recognition and rights, or their particular qualities or practices that can be conducive to peace and prosperity. These ideas constitute, through great variety of interconnected UN agencies and global NGOs, a kind of composite vision of human life, a form of instrumental, rights-oriented, managerial, but, oddly, at same time, publicly engaged and participatory anthropology. The combination of ideas about human with ideas having to do with rights, dignity, security, and development is powerfully compelling, influencing not just officials and activists, but also public consumers of ideas about rights and their human subjects. As I intend to show here, involvement of these institutions in anthropological knowledge probably reaches a wider audience than (while drawing from and influencing) concepts of academic anthropology.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.302
Teacher spread0.297 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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