A Decade of Dedication: Giving, Giving, Giving… and Giving Up Teaching
Bibliographic record
Abstract
A decade of dedication: Giving, giving, giving… and giving up teaching Abstract In this paper we explore the circumstances leading to an elementary teacher’s decision to leave the profession. We analyse her narratives of eleven years of teaching using a storyline technique and a framework provided by the five aspects of teacher self-understandings (Kelchtermans, 2009) and the interplay between vulnerability, micro-political literacy and self-understanding. We suggest that, particular types of job perception predispose teachers to high levels of vulnerability. We suggest that teachers need, well as developing micro political literacy, to consider reaching an understanding of their personal giving capacity in teaching contexts, in order to manage vulnerability and negative impact of self understanding. Dix ans de devouement : don de soi et abandon de l’enseignement Dans cet article nous analysons les circonstances qui ont conduit une enseignante de niveau elementaire a abandonner l’enseignement. Nous analysons les recits dans lesquels elle raconte ses onze annees d’enseignement, au moyen d’une technique de recit et d’un cadre donne par les cinq aspects de la comprehension de soi chez les enseignants (Kelchtermans, 2009), et nous nous penchons egalement sur l’interaction entre la vulnerabilite, les connaissances micro-politiques et la comprehension de soi. Nous croyons que certains types de perception de leur travail predisposent les enseignants a une plus grande vulnerabilite. Outre les connaissances micro-politiques, les enseignants doivent developper une meilleure comprehension de leur capacite personnelle de se donner en contexte d’enseignement, afin de gerer leur vulnerabilite et l’impact negatif de la comprehension de soi.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".