Habermasian Discourse Theory for Educational Policymaking: Attending to Perspective Taking and Communicative Agency
Bibliographic record
Abstract
This paper identifies and considers issues of perspective taking and communicative agency in applying Jürgen Habermas’s discourse theory to policymaking in educational settings. The central question is whether Habermas provides an epistemic framework that supports reciprocal and sincere expressions of the views and interests of individuals in a heterogeneous society. Examining this question leads to a discussion of “practical discourse” in light of a willingness of participants to reach mutual understanding and agreement, and the centrality of perspective taking and communicative agency in such discourses. Also examined is a conceptualization of “application discourses,” the implications of such discourses for perspective taking and communicative agency, and the role these discourses might play in further assuring the overall inclusivity and context sensitivity of applying education policies in specific circumstances. The paper then gives a brief re-analysis of an empirical study that used Habermas’s concept of the “ideal speech situation” as a normative framework for interpreting data. The re-analysis means to illustrate the practical value of practical discourse for guiding and assessing educational policymaking. The paper ends with a short justification of the necessity of attending to perspective taking and communicative agency when viewing education as a basic human right.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".