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Record W2595073917 · doi:10.3138/jcfs.42.3.319

Genealogy and Archaeology: Analyzing Generational Positioning in Historical Narratives

2011· article· en· W2595073917 on OpenAlexvenueno aff
Claudia Lenz

Bibliographic record

VenueJournal of Comparative Family Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeInterpretation (philosophy)Perspective (graphical)ConsciousnessMeaning (existential)Identity (music)SociologyEpistemologyGenealogyHistoryAestheticsLinguisticsComputer science

Abstract

fetched live from OpenAlex

The article introduces an analytical perspective which can assist in charting generation-specific modes of sense-making and self-understanding in memory accounts, biographical and historical narratives. The point of departure is the double meaning of “generation” as it is found in the theoretic debate. On the one hand, generational positioning is informed by the genealogical sequence of parents, children and grand-children–corresponding with the intergenerational transmission of memories. On the other hand, we the fact of being part of a specific age cohort provides a framework for the interpretation of former experiences, mirrored in Karl Mannheim’s concept of the generation as a community of shared experiences. The two modes of generational selfpositioning, which are coined as “genealogical” and “archaeological” here, inform processes of historical sense-making and interpretation. Which type of generational “logic” applies has a major impact on the ways in which coherent and meaningful links between past, present and future are constructed. Tracing up generational positioning in memory accounts and narratives of the past can, thus, be used as an analytical tool for getting access to narrative patterns–and to the functioning of historical consciousness as a provider of identity and orientation. This will be illustrated below with empirical evidence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.331

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 studies0.0000.001
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 head0.432
GPT teacher head0.452
Teacher spread0.020 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2011
Admission routes1
Has abstractyes

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