Recovering the (Mis)Promises of Critical Pedagogies in Neoliberal Times: A Turn to Ethics
Bibliographic record
Abstract
There are many renderings of critical pedagogy and at the core of each theory is a desire for the common good and a more just world. However, neoliberal logic is undermining the foundational promises of critical pedagogies. What happens when teachers, schooled in these theories—these promises—work in buildings that are hostile, indifferent or simply pay lip service to such theoretical goals? What happens when teachers realize, witness and participate in education as a force of ongoing colonial and systemic oppressions based on sex, gender, class and race (Battiste, 2005; Dion, 2010; Gaztambide-Fernández, 2011; Noroozi, 2017)? What happens to teachers’ (critical) pedagogical praxis when all hope of change seems impossible, when the inevitability of the way-things-are sucks all hope out of you? In an attempt to grapple with the “pervasive atmosphere of capitalist realism” (Fisher, 2009, p. 16) that infiltrates and impedes the educational, I position public education in Berlant’s (2011) notion of cruel optimism and question the (mis)promises of critical pedagogies. In doing so, I consider a turn to ethics to recover the “educational in education” (Di Paolantonio, 2016, p. 148). I make this move to think through how an ethics of responsibility to and for others might be fostered in schools given the compromised place of public education today.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.092 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".