Re(Imagining) Teacher Preparation Through Symbolic Interactionism and the Looking-Glass Self
Bibliographic record
Abstract
In reading the article, (Re)Imagining Teacher Preparation for Conjoint Democratic Inquiry in Complex Classroom Ecologies, the theory of symbolic interactionism from the field of Sociology came to mind.According to the theory of symbolic interactionism there are no objective realities, only multiple realities based on actors' interactive experiences and definitions of the situation.An actor's reality is created over time and founded upon numerous interactions in society.These interactions become internalized and shape or mold an actor's reality that is used to form an actor's identity.This reality is not necessarily permanent because an actor's identity can change as more interactions with different people, groups, organizations, and institutions occur, yet past interactions and an actor's preexisting definition of reality can impede changes to the actor's evolving definition of reality.One of the objectives of a teacher-student interaction is to alter students' subjective definition of reality.This can be achieved through quality rapport that utilizes reflexive practice.A sub-theory within the larger umbrella theory of symbolic interactionism is Cooley's Looking-Glass Self theory.The Looking-Glass Self theory involves the idea that a person shapes their own self concept out of how they imagine others perceive them.I first learned about the Looking-Glass Self during graduate school, when I met my
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".