The Erosion of the Public Good: The Implications of Neo-Liberalism for Education for Democracy
Bibliographic record
Abstract
This article describes the meaning, history and significance of the concept of the ‘public good’. It begins by theorising the ‘public good’ in relation to literature in the field, particularly Dewey. The public good is understood as an imagined and communal space in which goods valued by society become collectively owned and shared through respectful and open contestation and negotiation. The argument is then made that schools are both part of the public good as well as involved in the development of this concept in students, but that the ability of schools to do this is being damaged by new discourses. Current research and literature in the field of education is used to demonstrate how neo-liberal ideology is eroding this democratic idea. For example, neo-liberal ideology incorrectly positions all goods (including education) as private goods, with damaging consequences for society generally. Its controlling policies negatively affect the ability of schools to educate students about and for the public good, within a democratic conception of society. The article concludes with recommendations that aim to reinvigorate education for the and as a public good in schools. These recommendations are focused on teaching pedagogies.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".