‘The Antidote to Hate Is Success’: An Interview With Izzeldin Abuelaish
Bibliographic record
Abstract
Izzeldin Abuelaish, often referred to as “the Gaza doctor,” grew up in a refugee camp in the Gaza Strip. He overcame poverty and many other obstacles, and was accepted to medical school in Cairo. He became an internationally recognized expert on issues of fertility and worked in an Israeli hospital. Dr. Abuelaish married and had eight children. Shortly after his wife died from leukemia, he was with seven of his eight children in their home in Gaza on January 16, 2009 when their home was hit by a mortar during Israeli shelling. Three of Dr. Abuelaish’s daughters and his niece were killed instantly, and another daughter was profoundly wounded. Despite his great pain, he held to the belief that hate is not an appropriate response to war. Today Dr. Abuelaish, who has been nominated for the Nobel Peace Prize, has started a foundation called Daughters for Life (www.daughtersforlife.com), which works to educate young women from the Middle East in an attempt to promote peace. In addition, he has written a book entitled I Shall Not Hate: A Gaza Doctor’s Journey on the Road to Peace and Human Dignity (Random House Canada, 2010), in which he recounts his life story and his philosophy. Journal editor Joanie Eppinga spoke with Dr. Abuelaish at the Gonzaga University Institute for Hate Studies’ Conference on Hate Studies in Spokane, Washington, on April 7, 2011.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".