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Record W2264130948 · doi:10.1017/s0144686x15001415

The relational making of people and place: the case of the Teignmouth World War II homefront

2016· article· en· W2264130948 on OpenAlexaff
Gavin J. Andrews

Bibliographic record

VenueAgeing and Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipPoliticsPerceptionSpace (punctuation)Media studiesPeriod (music)SociologyHistoryPolitical scienceSocial scienceAestheticsLawPsychologyArt

Abstract

fetched live from OpenAlex

ABSTRACT Building on the pioneering research of a small number of gerontologists, this paper explores the rarely trodden common ground between the academic domains of social gerontology and modern history. Through empirical research it illustrates the complex networking that exists through space and time in the relational making of people and places. Indeed, the study focuses specifically on the lived reality and ongoing significance of life on the small-town British coastal homefront during World War II. Seventeen interviews with older residents of Teignmouth, Devon, United Kingdom, investigate two points in their lives: the ‘then’ (their historical experiences during this period) and the ‘then and now’ (how they continue to reverberate). In particular, their stories illustrate the relationalities that make each of these points. The first involves residents’ unique interactions during the war with structures and technologies (such as rules, bombs and barriers) and other people (such as soldiers and outsiders) which themselves were connected to wider historical, social, political and military networks. The second involves residents’ perceptions of their own and their town's wartime histories, how this gels or conflicts with public awareness, and how this history connects to their current lives. The paper closes with some thoughts on bringing together the past, present and older people in the same scholarship.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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