Do You See What I See? Universal Translation, the Postmodernist Lens, and Implications for Educational Research
Bibliographic record
Abstract
Abstract: In this philosophical paper, I make a case for the enduring utility of postmodernism as a lens through which to critique basic assumptions about knowledge and reality. I use this lens to tackle the contradictions built into so-called universal norms and values, which I argue are necessarily local and particular. This focus on the paradoxical structure of the universal informs my exploration of two related issues—the sense of practical paralysis, or inability to act, that seems to accompany postmodernism’s incredulity toward universals, and the role of the ethical educational policy researcher, paralysis notwithstanding. Accordingly, I problematize universalist claims about principles of human resilience as well as access to formal education, with an eye to the indefinite suspension of truth-claims. I contend that educational policy researchers have a responsibility to actively engage with the tensions of philosophical problems and conclude by suggesting what sorts of ethical imperatives might be cultivated by a responsible educational policy researcher in working to reconcile the so-called truths of science and the doubts of postmodern philosophy.
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 | 0.045 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.145 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| 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".