Discrimination against the Poor in Law and Practice: The Poor as a Vulnerable Group
Bibliographic record
Abstract
I wish to thank Professor Tanya Monforte, my thesis supervisor, for her constant guidance, assistance and support whether academic or moral. Without her, I don't think I would be where I am today both academically and intellectually. I would also like to thank Ms. Diana Van Bogaert, who has always been there for us from the very first day in this program. Her very much appreciated assistance and review of the thesis in the final phase of the writing process has been of enormous help and for that I'm forever grateful. A very special thanks I would like to give to Professor Alejandro Lorite, who has been of great inspiration to me and of great support. A special thanks I would also like to give to Shimaa and Fathi, whose condition of poverty was the main inspiration for this thesis. I'm particularly thankful to Mr. Mohammed Abdel Salam for providing me with countless academic sources and for his very much appreciated help during the field research of this thesis. I am also grateful to my friends Reem Wael, Noha Wagdy and Austin Power, whose support is very much appreciated during our trip to Montreal, where I presented a paper on my thesis at the Law and Society Conference. I'm especially thankful to my very dear friend Layla Kamal, who has never let me down and has always been there for me both as a friend and sister. Without her continuous encouragement to follow my dream, I wouldn't probably be where I am right now. I'm also thankful to my dear friend Irene George for her moral support during the toughest time of the writing process of this thesis. Finally, I would like to thank my dear friends Sherouk Abdel Ghaffar, Ray Wung, Michael Mohsen, Nevine Henry, Christina Hanna, Donia Nagi, Michelle Strucke, Noha Ali, and Marwa Maraei for their constant encouragement and support and for believing in me.
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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 | 0.003 | 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) | 0.001 | 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".