Dhammika Herath and K. T. Silva, Eds.: Healing the Wounds: Rebuilding Sri Lanka after the War
Bibliographic record
Abstract
Dhammika Herath and K. T. Silva, eds. Healing the Wounds: Rebuilding Sri Lanka after the War. Colombo and Kandy, Sri Lanka: International Centre for Ethnic Studies, 2012. 209 pp. Photographs. Graphs. Index. $15.00 he. Healing the Wounds has come out at a time when Sri Lanka is seriously grappling with the problem of nation building after 30 years of internecine war between the Sri Lankan government forces and guerilla forces of the Liberation Tigers of Eelam (LTTE). The official war ended in 2009 making the majority Sri Lankans jubilant while the diaspora communities in the Western world felt disheartened. In the introduction, Herath indicates that this book was an outcome of a critical look at the post-war rebuilding of the Sri Lankan society by six social science disciplines. Most of the data discussed in the book emanate from an action research project executed in war-torn areas in the country. Chapter presents a thought-provoking essay by Jayadeva Uyangoda. This essay addresses the current impasse in the country regarding a solution to the ethnic conflict. He has brilliantly argued that thinking along the lines of Sinhalese Patriotism or Tamil Homeland Sentiments is not going to bring effective ammunition to re-cast the Sri Lanka nation-state project. Uyangoda has borrowed Charles Taylors' phrases such as two solitudes and deep diversity regarding the English-French Canadian scene to illustrate his case while emphasizing the fact that Sri Lanka needs to build a political nation, not a Cultural Nation. In chapter 3 Kalinga Silva addresses an issue many people paid lip service to in the past. The issue is what demographic changes have occurred as a result of the war? According to Silva, there have been many demographic imbalances and distortions. Some of the distortions include: distorted sex ratios, age structures, a high ratio of military personnel compared to civilians, a high rate of women-headed families, a high rate of disability, and a high rate of family breakdowns. The data for this chapter come from the 2011 National Enumeration of Vital Events conducted by the government. It appears that surviving women are worst affected in the war in terms of poverty, personal security and marriage, while many men have become direct victims of the war. However, Silva has pointed out that in the Northern Province and parts of the Eastern Province, women have also risen to important positions in civil administration and in local government. The next chapter written by Dhammika Herath examines both social and cultural consequences of the war. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".