“There is no home for me in this world”: States of statelessness and the need to deterritorialize human rights
Bibliographic record
Abstract
There are millions of people in the world today who ‘reside’ in no place. Technically, though, no place simply means a non-existent place, which by extension annuls the existence of these people. May of these are from areas where they not recognized as citizens, and from where they have been uprooted because of war and related upheavals. In these instances, these have no other choice but to seek other places to live. It is indeed, when the doors are shut and they are not accepted in the new places that they become denationalized and trapped in ‘no-man’s land.’ Such acts of exclusion are dehumanizing, painful and should not be accepted in our world. In this paper, I argue that this widespread problem exists due to lack of basic citizenship and human rights possibilities. The problem is also attached to specific understandings of sovereignty and human rights where, in the case of a conflict, for example, whatever the nation-state almost always takes precedence over the fundamental rights of the individual and/or groups. Indeed, this problematic reality is discursively (as well as pragmatically) located within the human rights, and related governance debates, which always display some form of conceptual and practical disconnect between the nation-state’s right to refuse entry, and an individual’s right for asylum.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".