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Record W4239762118 · doi:10.3386/w20291

Unhappy Cities

2014· report· gl· W4239762118 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typereport
Languagegl
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaInstitute for Humane Studies, George Mason UniversityNational Science Foundation
KeywordsGeography

Abstract

fetched live from OpenAlex

There are persistent differences in self-reported subjective well-being across U.S. metropolitan areas, and residents of declining cities appear less happy than other Americans. Newer residents of these cities appear to be as unhappy as longer term residents, and yet some people continue to move to these areas. While the historical data on happiness are limited, the available facts suggest that cities that are now declining were also unhappy in their more prosperous past. One interpretation of these facts is that individuals do not aim to maximize self-reported well-being, or happiness, as measured in surveys, and they willingly endure less happiness in exchange for higher incomes or lower housing costs. In this view, subjective well-being is better viewed as one of many arguments of the utility function, rather than the utility function itself, and individuals make trade-offs among competing objectives, including but not limited to happiness.

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.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.554
GPT teacher head0.551
Teacher spread0.003 · 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