The power of stories to democratize classroom discourse and drive authentic leadership development
Bibliographic record
Abstract
The pedagogical strategies we adopt as engineering educators are not simply tactical instruments used to get the job of knowledge transfer done. They are relational processes that shed light on who we are as educators and how we connect our students to their chosen profession. In this paper I draw on five conceptual anchors from Paulo Freire’s and bell hooks’ critical pedagogy: decentring teacher authority [1, 2], reading the world with the word [3], student engagement [2], theory as liberatory practice [4], and drawing on experiential wisdom [5] to analyze four activities I used in an elective HSS course called “The Power of Story: Discovering your Leadership Narrative.” The primary purpose of this paper is to illustrate what is possible when we supplement professional skill development with critical reflection on practice. I conclude that at least three things are possible: 1) students recognize their experientially rooted wisdom, 2) the classroom shifts from lecture hall to learning community, and 3) HSS credits are transformed from program accreditation instruments to catalysts for deeply disciplined inquiry.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".