The relational making of people and place: the case of the Teignmouth World War II homefront
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".