The Impact of Covid–19 on Ethnic Minorities in Sri Lanka
Bibliographic record
Abstract
The unforeseen impact of Covid–19 and its outcomes, including a variety of state responses, have directly or indirectly affected all segments of human society in multiple ways. Most importantly, certain communities have been more distressed than others. In this global context, Sri Lanka seems to be among the countries where the negative impacts of Covid–19 on ethnic minorities have been more severe and intemperate. The article’s overarching research question concentrates on the Sri Lankan government’s responses to the pandemic and their unequal impact on some ethnic groups since the first quarter of 2020 through 2021. This qualitative study finds that the spread of the virus extended and intensified the inequalities, frustration and discontent among ethnic minorities, as the experience of uneven impacts is clearly and directly associated with already-entrenched injustices that prevent the benefits of mainstream socio-economic processes from reaching certain Sri Lankan ethnic minorities. It is likely that this situation will continue well into the post-pandemic recovery stages. The article therefore concludes that Sri Lanka needs to undertake a coordinated, consultative process founded on the principles of equality, equity, social justice and human rights, to develop policies and strategies to address issues that rendered the sufferings of ethnic minorities severe during both the pandemic and the post-pandemic recovery stage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".