Conceptualizing a Personalized Identity-Focused Approach to Teacher Professional Development: Postulating the Realization of Reform
Bibliographic record
Abstract
Identity structure analysis (ISA) reveals core and conflicted identity constructs, long-term aspirant goals for behaviours, behaviours one wishes to avoid, potential for behaviour change when moving from one life domain to another, and people that are the subject of identity conflicts. In this work, ISA is applied to a teacher’s identity to form a framework to guide professional development. A rationale for use of the ISA framework is developed that connects it to calls for reform in professional development. Keywords: teacher identity; identity structure analysis; professional development; mentoring; teacher education L’analyse de structure identitaire (ASI) révèle des constructions identitaires fondamentales et divergentes; des objectifs à long-terme relatifs au comportement; des comportements que l’individu désire éviter; le potentiel pour un changement comportemental lors du mouvement d’une sphère de la vie à une autre; et des gens qui vivent des conflits identitaires. Cet article porte sur l’application de l’ASI à l’identité d’un enseignant de sorte à fournir un cadre pour guider le développement professionnel. Nous développons un motif pour l’utilisation du cadre d’ASI qui le lie aux demandes pour des réformes dans le domaine du développement professionnel. Mots clés: identité d’enseignant; analyse de structure identitaire; développement professionnel; mentorat; formation des enseignants
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".