“Small” Stories and Meganarratives: Accountability in Balance
Bibliographic record
Abstract
Background/Context Meganarratives, or “grand stories,” are composed of loosely held ideas about standardization, the rhetoric of education for all, the focus on individual success, and the appearance of representative diversity that rarely take into account human diversity embedded in deeply rooted value systems and authentically present in “the realm of face-to-face relationships.” Purpose/Objective/Research/Question/Focus of Study In this article, we offer atypical, noncanonical “small” stories as accounts of ways in which teachers and students live in small moments of diversity unseen and unheard within prevailing meganarratives of accountability. Setting This research took place in the mid-southern United States and eastern Canada. Population/Participants/Subjects Research participants included a preservice teacher candidate in Canada and an in-service teacher in the United States. Research Design Through using narrative inquiry as a human research method, we feature small storied nuggets of teachers and students breaking through “surface equilibriums and uniformities” to challenge educational orthodoxies that cast long shadows on their work and their relationships and add to the complexities of their lives. Conclusions/Recommendations In the final analysis, we argue for fluid back-and-forth movement between small stories and meganarratives in order to nurture dialectical relationships between and among theory, practice, and policy. Such an approach would create spaces for experiences of accountability to be lived and told, and relived and retold, in more balanced ways.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".