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Record W3196274877 · doi:10.33612/diss.178288002

Lives on Edge: everyday practices of people experiencing poverty in disadvantaged areas

2021· dissertation· en· W3196274877 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedPovertyEveryday lifeContext (archaeology)EthnographyGeographyInequalitySociologyEconomic growthGender studiesPolitical science

Abstract

fetched live from OpenAlex

Growing socio-spatial disparities in Western countries is a worrying trend which harms opportunities for a better future, particularly for people experiencing poverty. While the causes for growing socio-spatial inequalities are connected to global economic currents, the polarizing consequences are experienced in the context of everyday life, by ordinary people in cities, towns, and streets. 'Lives on Edge' aims to comprehend the ways in which people experiencing poverty are affected by increasing socio-spatial inequalities in the context of their everyday lives. Based on three ethnographic case studies in the city of Groningen (the Netherlands), the city of Calgary (Canada), and the rural region of the Groninger Veenkoloniën (the Netherlands), this thesis paints a detailed picture of the everyday practices of people experiencing poverty in these disadvantaged areas and how local socio-spatial developments relate to their experiences. The ethnographic evidence from these case studies demonstrates that participants feel that their everyday practices are increasingly marginalized and oppressed with respect to their social and spatial surroundings. An exacerbating factor in this marginalizing process is the tenacious focus of local socio-spatial developments on appearance, rather than focusing on achieving better prospects for vulnerable residents. As a result, the lives of people experiencing poverty are increasingly on edge.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.277
Teacher spread0.263 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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