Stories in the Classroom: Building Community Using Storytelling and Storyacting
Bibliographic record
Abstract
In this article I describe how, over the past school year, I have witnessed a diverse, culturally mixed group of kindergarten children, who were strangers to one another, join hands and voices as they tackled particular challenges together. I attribute this togetherness to our daily experimentation with storytelling and storyacting (Paley, 1981, 1984, 1990, 1992, 1995, 1997, 1999, 2001, 2004, 2010). As if pieces in a puzzle, many storytelling and storyacting moments helped the students to cooperatively “click” by enhancing their relationships with one another and strengthening the sense of community in the classroom environment. I see reciprocity in the relationships as the students listen to each other’s needs and ideas and, in the process, have their own voices heard. The outcome of this reciprocity has been a noticeable feeling of community and kinship in the classroom arising out of a sense of empathy, understanding, friendship, and acceptance and resulting in greater self-confidence and a sense of security for the children as individual members within the community.
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.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".