Exploring the Structure and Process of Large Group Meeting Time From an Ethnographic Perspective
Bibliographic record
Abstract
The purpose of this study is to explore everyday large group meeting interactions in the preschool classroom. The data were collected over a five and a half month period in one preschool classroom, using an ethnographic research approach. The ethnographic research was mainly based on a socio-cultural theoretical orientation, an understanding that the nature of children’s classroom experiences is socially constructed in the course of everyday life. Multiple methods were used to gather data, including participant observation, field notes, interviews with teachers, and video recordings of classroom interaction during large group meeting time. In this preschool classroom, the large group meeting time, Morning Meeting, consisted of five sub-events. Along with the typical ritual-like characteristics of some sub-events (i.e., calendar/weather), the sub-event of “large group conversation/talk” was a context in which various types of group interaction between the children and teacher could be observed, which allowed the large group time, Morning Meeting, not to proceed in one fixed manner in this classroom. Through micro-level analysis of the data, four different types/purposes of interaction could be identified. The “large group conversation/talk” subevent in this classroom was: 1) a context for individuals to share personal stories, news, and artifacts that they had brought from home with the large group; 2) a context for the teacher to make announcements about upcoming events to the group; 3) a context for making group meaning of various child-initiated and teacher-initiated topics through collaborative and extensive group conversation; and 4) a context for teacher-directed interaction on certain topicsthat the teacher chose to introduce to the children. Along with the typical ritual-like characteristics of some subevents (i.e., calendar/weather), the preschool large group meeting time served as an interactive context for the children and teacher to engage in meaning making of a socially constructed nature through their large group interaction. The complex nature of large group interaction was revealed through analysis of the children’s and teacher’s actual interaction during Morning Meeting events.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".