Deprofessionalisation as a Performance Management Dysfunction: The Case of Inclusive Education Teachers in Russia
Bibliographic record
Abstract
This article examines two important phenomena related to the performance-based assessment challenges of the teaching profession: deprofessionalisation and dealtruisation. Theoretical analyses have allowed us to draw conclusions concerning the trends in the teaching profession, its relations with dealtrusation and deprofessionalisation, and the various contradictions associated with the performance of professionals in their social role during performance-based reforms. Based on Merton’s methodology of altruism research—and partly debating with modern approaches to deprofessionalisation—we have chosen inclusive education teachers as a special group, which under the influence of dysfunctional performance management requirements became dealtruistic to the greatest extent. In this study, convenience sampling and in-depth interviews have been used. The sample consisted of 57 inclusive education teachers. Data processing was carried out with the use of Corpus Tool 3.1.14. As a result, based on the typology of altruistic behaviour, as introduced by Merton, we have identified the type of behaviour amongst teachers, which leads us to the formulation of educational and school policy recommendations. The authors suggest that a more in-depth study of the experience of other countries will help with the development of a more optimal version of educational reforms and its continuation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".