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Record W1511950416

Entrepreneurial Activity and Civil War in Colombia : Exploring the Mutual Determinants between Armed Conflict and the Private Sector

2010· preprint· en· W1511950416 on OpenAlexfundno aff
Angelika Rettberg, Ralf J. Leiteritz, Carlo Nasi

Bibliographic record

VenueRepositorio Institucional E-DocUR (Universidad Del Rosario) · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
FundersDepartment for International DevelopmentUnited Nations University World Institute for Development Economics ResearchInternational Development Research CentreUlster UniversityStyrelsen för Internationellt Utvecklingssamarbete
KeywordsArmed conflictPrivate sectorSpanish Civil WarCivil ConflictPolitical scienceDevelopment economicsBusinessEconomic growthEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

As elsewhere, the Colombian private sector has been accused of promoting or profiting from violence in the country. However, the private sector's role in the armed conflict and the impact of conflict on entrepreneurial activity vary, as reflected by differences in political activism, in peacebuilding strategies and in costs endured according to company size, sector, and region of operations. At the same time, accounts of regional variation in conflict intensity suggest that an understanding of the Colombian confrontation requires a subnational approach. This paper explores whether and how differences in regional armed armed conflict can be attributed to differences in entrepreneurial make-up and activity associated with five natural resources, produced in different regions (oil, coffee, bananas, emeralds, and flowers). This paper suggests that company-specific traits, institutions of production, and the nature of international markets have a significant impact on the link between entrepreneurial activity and armed conflict in Colombian regions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
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 head0.049
GPT teacher head0.240
Teacher spread0.191 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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