Misunderstanding Opportunities: (Post-)Resettlement Issues in the Recea Neighbourhood of Alba Iulia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".