What’s so important about teachers’ working conditions? The fatal flaw in North American educational reform
Bibliographic record
Abstract
Teaching conditions have been an enduring concern for North American teachers for over a century. This paper explores this phenomenon by tracing how teaching conditions have been understood by decision makers and in educational research over time. It draws on historical research on the formation of mass public education systems to consider why the conditions teachers identify as critical to their work have been so persistently ignored by policy makers and researchers. Then we review the major ways that teaching conditions have been understood to matter in educational research, focusing first on psychological understandings about the relationship between working conditions and teacher motivation and then on the organizational factors teachers identify as critical to their sense of efficacy and job satisfaction. These two conceptualizations, however, are limited in their explanatory power because they are embedded in a bureaucratic framework where teachers are understood primarily as implementers of policy decisions made by their organizational superiors. Attempting to understand the full power of teaching conditions requires a more comprehensive understanding of teaching and learning processes closer to the ground. We provide descriptions of teachers’ work emerging from a recent study in order to demonstrate the close and reciprocal relationships between teaching conditions and students’ opportunities to learn.
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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 | 0.013 | 0.025 |
| 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 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".