Bibliographic record
Abstract
Over the past three decades, student debt has surged in most English-speaking countries. Despite this trend, enrollment at universities has steadily risen, which testifies to the enduring appeal that higher education still exerts on middle-class individuals and families. Nevertheless, the policies responsible for this growing indebtedness have also stirred up their share of controversy. In an attempt to make sense of these debates, this article focuses mainly on the English and American cases to demonstrate how moral arguments have been put forth both in favor of and against using credit to finance education. Because it shows the ambivalent and unstable nature of the normative principles underpinning debt relations, the concept of moral economy is particularly well suited to discuss this phenomenon. On one hand, it explains how certain ethical expectations seemingly justify the need to incur debt for educational pursuits. On the other hand, it also shows why the perceived legitimacy of student debt eventually comes to be challenged. At its core lies an undecidable choice between antinomic values, as the decision to study on credit involves both a striving for personal autonomy and subjecting oneself to an exploitative relation of dependence. Recognizing this fundamental ambivalence, the article develops a ‘pharmacological approach’ to address the issue of student debt, aiming to better grasp the social and political tensions surrounding higher education in neoliberal societies.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".