Bibliographic record
Abstract
Jordy Cummings 1 (JC): This issue of Alternate Routes has the theme of "Capitalism in the Classroom."So to begin I'd like to ask you, in the broadest sense, what you see as the effect of capitalism in its various forms in the classroom.Did neoliberalism introduce a different logic?What about post-crisis capitalism?Jim Silver 2 (JS): In the last thirty to forty years, the era of neoliberalism, capitalism has done a lot of damage to education.Consider the case of universities.Public funding has been systematically reduced in real terms over a long period.Tuition is rising in real terms, reducing access for many, but especially for those already on the margins of our society.A high and still rising proportion of classes are taught by nontenured faculty who are part-time, poorly paid and have minimal job security.Universities are increasingly corporatized -management is less collegial and more top-down, and private sector fund-raising has assumed enormous importance, adding to the influence exercised by corporations and wealthy individuals.An even greater problem lies outside the classroom.One of the strongest correlations in the social sciences is that between income and educational outcomes.The higher the income, the better are educational outcomes; the lower the income, the worse are educational outcomes.Those who grow up in poverty are much less likely to succeed educationally than those who are economically better off.Since a major consequence of the neoliberal era is the growth of poverty and inequality, more and more people are left behind educationally.This is worsened by the 1
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".