From Senge to Habermas: Reconceiving “Discourse” for Educational Learning Organizations
Bibliographic record
Abstract
The Art and Practice of the Learning Organization, revised in 2006, remains highly influential in educational administration. 1The extent of its influence can be gauged by its direct adaptation in Schools That Learn: A Fifth Discipline Fieldbook for Educators, Parents, and Everyone Who Care About Education. 2Senge's main contribution to educational administration is his definition of learning organizations as "organizations where people continually expand their capacity to create the results they truly desire" (FD, 3).Administrative interest in building educational learning organizations is often supported by statements such as Michael Fullan's identification of school development as "changes in schools as institutions that increase their capacity and performance for continuous improvements." 3Whether or not one favors the learning organization model, the fact remains that Senge is routinely granted the status of "guru" in educational administration, ensuring his sway over current and future administrators.At issue in this essay is whether Senge's explanation and application of various modes of linguistic communication are sufficient within the context of public education; specifically, should Senge's conception of "discourse" be applied to administrative practice and policy without being reconceived from a moral and epistemic perspective?My stance is, "No."To support my position, I present both Jürgen Habermas's principle of universalization and his "discourse ethics" for consideration in the process of reconceiving the notion of discourse for educational learning organizations.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.098 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.018 |
| 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".