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Bordering Wastelands

2014· article· en· W4214844953 on OpenAlexaff
Kateryna Pashkovska

Bibliographic record

VenueSotsiologicheskoe Obozrenie / Russian Sociological Review · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNationalityContext (archaeology)Space (punctuation)Political scienceGeographyState (computer science)SociologyLawArchaeology

Abstract

fetched live from OpenAlex

In this anthropological study, I examine how a particular state and regional border is crossed in the context of a joint socio-ecological project concerning recycling waste in Karelia. During a two-year, multilevel project, cooperation developed between the Petrozavodsk municipality and its northern partners under the auspices of the Nordic Council of Ministers. This cooperation was advanced particularly through the eastward translation of values, including early education and sustainable behavior, which were consistent within broader international border relations across the Barents Euro-Arctic Region. The process of the taking over of these imported Nordic values and knowledge, and adapting them to the everyday and professional life of the local participants of the project went hand in hand with the perpetuation of cultural cross-border stereotypes. The idea of marking home from foreign became equally applicable to the space within a much smaller entity, such as a condominium, a round-table in a discussion room, or a city flowerbed, particularly when the construction of the border intersected with the construction of the other. Invisible barriers, as well as physical objects, can demarcate the divide between individuals of the same nationality and cultural background who need to claim, protect, and reconstruct a personal connection to a piece of land.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.067
GPT teacher head0.421
Teacher spread0.354 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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