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Record W4327735950 · doi:10.33423/jlae.v20i1.5881

Public and Engaged Anthropology: The Legacy of Nina S. de Friedemann

2023· article· en· W4327735950 on OpenAlexaboutno aff
Greta Friedemann‐Sánchez

Bibliographic record

VenueJournal of Leadership Accountability and Ethics · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, violence, and history
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropologySociologyContext (archaeology)MetisEconomic JusticeGender studiesHistoryPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

Nina S. de Friedemann (1930-1998) was a public anthropologist. She practiced engaged research, with a view to promoting social justice for the communities with whom she collaborated and studied, and she anticipated the public anthropology of the North Atlantic academia by five decades. A pioneer in Afro￾Colombian studies and in visual anthropology, she documented and defended the cultural contributions of Black populations to the identity of an ethnically diverse Colombia. Friedemann’s fundamental work inspired leaders of the Black communities in their demands that culminated in Law 70 of 1993, also known as the ley de negritudes. Her research materials are housed at the Luis Angel Arango Library under the name Fondo Nina S. de Friedemann, a repository available for study. Using unpublished materials, correspondence, publications, and photographs, Greta Friedemann-Sánchez reflected on three pillars of her mother’s ethical legacy within the contemporary normative framework for the protection of human subjects and the historical context during which Nina S. de Friedemann worked as an anthropologist.

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)0.016
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0110.029
Scholarly communication0.0090.010
Open science0.0010.006
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.374
GPT teacher head0.404
Teacher spread0.030 · 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
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
Published2023
Admission routes1
Has abstractyes

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