Postmodern Paradox: Artificial Intelligence, Pedagogy and the Return of Robot Slavery
Bibliographic record
Abstract
Despite the contributions that postmodernism has made to teaching and learning in the computer age, several scholars and practitioners in education persist in proclaiming its demise or death. This philosophical survey challenges this argument by recalibrating Jacques Derrida’s and Jean-François Lyotard’s contributions to postmodern thought as complementary meditations on the simultaneity of differences. With this reset in mind, one discovers that the evidence critics use to substantiate the end of postmodernism in education is often tenuous and paradoxical. In fact, the simultaneity and indeterminacy at the core of postmodern thinking make it indispensable in contemporary debates on the dichotomy between human and non-human entities, especially as artificial intelligence and robots become increasingly efficient partners and rivals in our classrooms and workplaces. While robot slavery has been introduced as a resolution to the binary opposition between humans and non-humans, postmodernism reminds us that this remedy is contentious and not new. Before robots such as Figure 02 and Mobile ALOHA, there was Rastus Robot, a technological innovation that courts the idea of a black mechanical slave. This study reveals how postmodernism and technological advancements continue to inform our conversations about education and trouble the border between humans and the robot slaves of tomorrow.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".