“Small” Stories and Meganarratives: Accountability in Balance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it