Bibliographic record
Abstract
Millions of students in the United States are saddled with trillions of dollars in debt. The debt crisis is a behemoth, though, importantly, it is not monolithic. Experiences of student debt are unequal and uneven, and it is critical to study them as such to address them. There are many organizations bringing attention to the student debt crisis; however, there are surprisingly few institutions dedicated to studying it. Further, there are few studies that link the student debt crisis to other competing, nested crises of the present (e.g., climate change). Using theories of debt and indebtedness to contextualize the student debt crisis, this paper utilizes auto-ethnographic accounts of student debt – as a student debtor and faculty member – and ‘gray literature’ (reports, policies, and statistics) to highlight and analyze the uneven geographies of student debt in the US. The aim of this paper is to argue that a geographic perspective is generative for studying student debt because it allows for a more nuanced understanding of where and why student debt exists and persists with the intention of complementing ongoing activism to abolish student debt. This paper concludes with four potential pathways for future geographic research on student debt and a call for action.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.022 |
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".