MétaCan
Menu
Back to cohort
Record W7071995555

Transforming Bodies and Religions: Powers and Agencies in Europe

2020· book· en· W7071995555 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman sexualityEthnographyEmbodied cognitionPoliticsFocus (optics)
DOInot available

Abstract

fetched live from OpenAlex

This book sheds an interdisciplinary light on ‘transforming bodies’: bodies that have been subjected to, contributed to, or have resisted social transformations within religious or secular contexts in contemporary Europe. It explores the intersections of race, ethnicity, gender, sexuality and religion that underpin embodied transformations. Using post-secularist, postcolonial and gender/queer perspectives, it aims to gain a better understanding of the orchestrations and effects of larger social transitions related to religion. This volume is the outcome of the intensive collaboration of the authors, who for years have been meeting regularly in Utrecht, the Netherlands, to discuss themes related to religion and ‘the challenge of difference’, with an added afterword by Prof. Pamela Klassen from the University of Toronto. The book is divided in three subsections that focus on particular types of embodiment: body politics in governmental and NGO organisations; the role of the body in literary and/or autobiographical narratives; and ethnographic case studies of bodies in daily life. Doing so, it provides an innovative exploration of contemporary religion and the body. It will, therefore, be of great interest to scholars of Religious Studies, Gender and Sexuality Studies, Post-Colonial Studies, Anthropology, Sociology, Theology, and Philosophy.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.015
Scholarly communication0.0100.004
Open science0.0000.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.223
Teacher spread0.203 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicQR Code Applications and TechnologiesFrench-language works237,207