MétaCan
Menu
Back to cohort

Reconceptualizing the Social, Environmental, and Political Hazards Associated With Conflict-Induced Displacement in the Republic of Georgia

2024· reference-entry· en· W4398183606 on OpenAlexaff
Suzanne Harris-Brandts, David Sichinava

Bibliographic record

VenueOxford Research Encyclopedia of Natural Hazard Science · 2024
Typereference-entry
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Socioeconomic and Political Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsPoliticsDisplacement (psychology)Political scienceThe RepublicGeographyPolitical economyEnvironmental protectionSociologyPsychologyLawPsychoanalysisEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Following the Soviet Union’s disintegration in the early 1990s, Georgia entered several ethnic conflicts with its autonomous regions, Abkhazia and South Ossetia, which have sought unilateral secession. In total, over 300,000 people—primarily ethnic Georgians—have been forced to flee, finding refuge in other areas of Georgia, and becoming internally displaced persons (IDPs). To date, displacement in the country has largely been framed as a conflict-induced phenomenon tied to several acute periods of violence. Yet the hazards IDPs face do not end following their initial displacement. Up to 45% of IDPs have found refuge in vacant, non-purpose-built buildings—so-called collective centers (also referred to as organized resettlement facilities for displaced persons, “დევნილთა ორგანიზებულად ჩასახლების ობიექტები” in Georgian)—including former factories, kindergartens, hospitals, and hotel-sanatoria. There, they are exposed to mold, contaminated soil, sewage, and other environmental hazards. Government mobilization to improve collective centers or relocate IDPs elsewhere has been slow, in part due to weak state institutions and a lack of resources, with the state heavily reliant on international aid. Historically, the state also had a vested interest in maintaining the status quo of precarious IDP housing given that the right of return is interconnected with Georgia’s sovereign territorial claims. The hazardous environmental conditions of IDP collective centers have, therefore, been politically weaponized by the government, showcased to domestic and international audiences alike as evidence of the urgency in unifying Georgia’s territory. Since the late 2000s—decades after initial displacement—the government has finally shifted its approach and IDPs are incrementally being granted tenure or being resettled in purpose-built housing. Yet this, too, has prompted a reconceptualization of the social, environmental, and political hazards associated with displacement. In the early 21st century, the environmentally hazardous living conditions of Georgia’s collective centers are being used to justify IDP forced evictions in areas prioritized for urban redevelopment. The result is secondary displacement and an erasure of IDPs’ local histories. In these ways, environmental hazards have become deeply entwined with the social and political aspects of internal displacement in Georgia. The loss of collective centers in prime real estate areas links to a different aspect of post-hazard reconstruction yet one also deserving of attention. IDP identity is embedded within these spaces and should not be simply erased by future development. In such situations, there are socioeconomic and political complexities beyond the acute and pragmatic needs of securing humanitarian shelter away from violence. Thus, the line between displacement-induced hazards and political and environmental ones is blurred, making distinct categorizations less useful. Understanding these interconnections relative to issues of governance, resettlement, housing provision, and urban renewal is crucial to effectively support Georgia’s IDPs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0040.001
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.330
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOxford Research Encyclopedia of Natural Hazard ScienceSame topicGlobal Socioeconomic and Political DynamicsFrench-language works237,207