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Record W4383110113 · doi:10.1386/cjmc_00074_1

A beautiful place: Postmigrant trajectories in and around Berlin’s Tempelhofer Feld

2023· article· en· W4383110113 on OpenAlexaff
Francesca Pegorer

Bibliographic record

VenueCrossings Journal of Migration and Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCasualBeautyLawnNegotiationContemplationGeographySociologyOpposition (politics)AestheticsPolitical sciencePoliticsSocial scienceEcologyArt

Abstract

fetched live from OpenAlex

Tempelhofer Feld, in Berlin, Germany, is a large urban park located in the south of the city. Returned to the residents as a public green area in the early 2000s, the park has since become increasingly popular. Plans for a development of the area (including turning part of the area into a residential compound) have been consistently met with strong opposition. This paper looks at how regular users (with a background of migration) engage with the park, across a variety of activities and social constellations. Even though, to a casual observer, the park doesn’t seem to offer much more than a flat sequence of grassy lawns and asphalt lanes, there is, among regulars, a strong consensus on the beauty of the place. It’s an experiential (as opposed to contemplative) beauty, that enhances people’s sense of emplacement, of being present in space, as well as their affective entanglement with it. Through their relationship with the park, and among themselves, the research participants reorient themselves as Berlin residents, negotiating and reconfiguring the often narrow confines of their situation as migrants.

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)0.000
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 score0.388
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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