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Record W2598496019

Introduction: Home, Land, Homeland and Home/Land

2016· book-chapter· en· W2598496019 on OpenAlexaboutno aff
Marion Arnold, Marsha Meskimmon

Bibliographic record

VenueLoughborough University Institutional Repository (Loughborough University) · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandCompendiumCitizen journalismCitizenshipParticipatory action researchPhoto-essayIdentity (music)PhotographyMedia studiesSociologyGender studiesVisual artsPolitical scienceGeographyArtAestheticsAnthropologyLawArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Home/Land: Women, Citizenship, Photographies is an extensive compendium of texts and images, combining scholarly, creative and critical writing on photography with new work in photography. The contributions to the compendium range from academic essays on fine art and documentary photographies to photo-essays, community-based and pedagogical photographic projects, personal testimonies, creative writing, activist interventions and accounts of participatory action research using photography. Home/Land is global in its reach, exploring women’s lives in Britain and other European nations, the United States, Canada, the Middle East, South Africa, Asia and Australia. Bringing together texts and images produced by an international group of feminist scholars, activists, artists and educators, the book demonstrates how women have used photographic practices to find places for themselves as citizens, denizens, exiles or guests, within or beyond the nation as currently conceived, and, in so doing, how they actively produce new and different forms of identity, community and belonging.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0620.016

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 head0.108
GPT teacher head0.362
Teacher spread0.254 · 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
Published2016
Admission routes1
Has abstractyes

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