Bibliographic record
Abstract
Two legal concepts have become fundamental to questions of resource allocation in the modern state: rights and queues. As rights are increasingly recognized in areas such as housing, health care, or immigration law, so too are queues used to administer access to the goods, services, or opportunities that realize such rights, especially in conditions of scarcity. This Article is the first to analyze the concept of queues (or temporal waiting lines or lists) and their ambivalent, interdependent relation with rights. After showing the conceptual tension between rights and queues, the Article argues that queues and “queue talk” present a unique challenge to rights and “rights talk.” In exploring the currency of rights and queues in both political and legal terms, the Article illustrates how participants discuss and contest the right to housing in South Africa, the right to health care in Canada, and the right to asylum in Australia. It argues that, despite its appearance in very different ideological and institutional settings, the political discourse of “queues” and especially “queue jumping” commonly invokes misleading distinctions between corruption and order, markets and bureaucracies, and governments and courts. Moreover, queue talk obscures the first-order questions on which resource allocations in housing, health care, or immigration contexts must rely. By bringing much-needed complexity to the concept of “queues,” the Article explores ways in which general principles of allocative fairness may be both open to contestation and yet supportive of basic claims of rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.095 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".