THE NATURE OF THE FIGHT AGAINST MALARIA EPIDEMIC DURING THE LATTER STAGES OF OTTOMAN ERA AND FIRST QUARTER CENTURY OF THE REPUBLIC
Bibliographic record
Abstract
One of the events that left deep scars on the history of humanity are epidemics. With their destructive and devastating powers, epidemics led to significant impacts and changes on the demographic structures, political, social, economic, cultural lives of communities. Hence, malaria, the subject of this study, is also a fatal epidemic which has been known since the times when humans began agricultural activities. Malaria turned into a mass epidemic issue in the army and in some settlements in the Ottoman Empire, particularly during the first quarter of the 20th century. Wars, political crises, migrations, famine, poor living conditions have all encouraged malaria epidemics. Ottoman Empire actually did take some significant measures against malaria but the difficult conditions of the time prevented those measures to be effective. Then during the early years of the Republic, the postwar nation was also going through difficult times and this coupled with the deficiencies in health infrastructure helped malaria to spread easily. Thereupon, the state declared a total national struggle, or an official war against malaria, with an obligation for all public and private elements to unite. This study has been conducted with the purpose of providing a general overview of the struggle against malaria during the latter stages of the Ottoman period and the first quarter of the century of the Republic, discussing the kind of steps taken, policies and practises developed, hence conveying a historic experience on battling malaria and other similar epidemics.
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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 | 0.000 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".