MétaCan
Menu
Back to cohort
Record W252852412

Misunderstanding Opportunities: (Post-)Resettlement Issues in the Recea Neighbourhood of Alba Iulia

2013· article· en· W252852412 on OpenAlexaboutno aff
Cătălin Buzoianu, Sebastian Țoc

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)GeographyPolitical scienceEconomic geographyRegional scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Although its gold mining project has been locked in public debates and permit reviews for over a decade, a Canadian-Romanian company privately negotiated with the inhabitants of Roşia Montană commune, Romania, to buy their households and lands, and resettle them in a specially built neighbourhood in the city of Alba Iulia. This paper suggests that while the paternalistic character of resettlement has allowed resettlers to partially keep their group identity, and partially to reconstruct it in relation with the host community, it was also based on a misunderstanding of the relationship between resettlers and the organiser of resettlement. Drawing on field research, the resettlement was studied as a “continuous process” spanning three years (2010-12), during which this paper identifies (1) the changes in lifestyle, (2) the mechanisms of community regeneration, and (3) post-resettlement initiatives of resettlers. Although greater living costs (utility bills, real estate taxes, transportation) and unemployment seem to be balanced by better living conditions and greater educational opportunities for their children, the ambivalent paternalistic aspect of the resettlement has negatively influenced the development of the new community. While at first community issues were unsuccessfully addressed to the company, recent public improvement initiatives by resettlers have caused tensions between the two sides.

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)0.003
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.046
GPT teacher head0.306
Teacher spread0.260 · 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 designTheoretical or conceptual
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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicUrbanization and City PlanningFrench-language works237,207