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Record W3114647120 · doi:10.5539/jpl.v13n4p147

The End of Sri Lanka’s Civil War and the Fall of the Liberation Tigers of Tamil Eelam (LTTE): A Critical Analysis of the Contributed Factors to the Defeat of the LTTE

2020· article· en· W3114647120 on OpenAlexvenueno aff
Mansoor Mohamed Fazil, Mohamed Anifa Mohamed Fowsar

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsTamilPolitical scienceTerrorismSpanish Civil WarHomelandPoliticsGovernment (linguistics)BattleDevelopment economicsPolitical economyLawSociologyAncient historyHistoryEconomics

Abstract

fetched live from OpenAlex

Sri Lanka came to the international limelight through the backdrop of its undesirable war against the Liberation Tigers of Tamil Eelam (LTTE) that lasted over three decades. The LTTE was formed as a social force, and then it transformed as a leading armed movement to forward their decades-long quest to set up a Tamil homeland in Sri Lanka. The government ended the LTTE’s secessionist struggle in May 2009 after a lengthy and bloody battle. Several national and international factors played a crucial role in ending the civil war sooner. The study used a qualitative method of inquiry to explore the key factors that led to the fall of the LTTE, a vigorous armed movement that attempted to set up a separate state in the Island of Sri Lanka. The findings show that strong political leadership, fortified security forces, implementing sophisticated national security strategies, the split of the LTTE and the global war on terrorism are the major factors that had a significant impact and contributed in the LTTE being defeated in 2009.

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)0.006
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0210.022
Scholarly communication0.0100.006
Open science0.0010.005
Research integrity0.0030.005
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 head0.016
GPT teacher head0.276
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 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
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
Published2020
Admission routes1
Has abstractyes

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