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Record W2061511084 · doi:10.3176/tr.2014.1.02

WINTER-CITIES AND MOOD DISORDER: OBSERVATIONS FROM EUROPEAN CITY-FORM AT THE END OF LITTLE ICE AGE; pp. 19–37

2014· article· en· W2061511084 on OpenAlexaff
Abraham Akkerman

Bibliographic record

VenueTrames Journal of the Humanities and Social Sciences · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicLandscape and Cultural Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMoodIce ageGeographyDemographyPhysical geographyPsychologyGeologyClinical psychologySociologyGlacial period

Abstract

fetched live from OpenAlex

The rise of modernity in Europe, from the close of the Renaissance to the Second Industrial Revolution, had spanned the period of the Little Ice Age, and was manifest by intensifying urbanization. Europeans in cities during cold days of the late LIA were able to seek warm shelter much easier than their forerunners in earlier times or their contemporaries in colonial America. But at higher latitudes during autumn and winter, daytime shelter deprived people of sunlight. The likely outcome, depression, had been a prominent trait among the founders of modern science and philosophy, many of whom lived in northern Europe. A rich source of perceptually stimulating spatial contrast, historic European city-form, compact and conducive to street walking, had been a visceral catalyst to intellectual exploration, while at the same time it had provided also a partial remedy to some of the mood disorder. Such observation is relevant to contemporary winter-cities.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.241
Teacher spread0.165 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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