Developing professional capital in teaching through initial teacher education
Bibliographic record
Abstract
Purpose – The purpose of this paper is to contrast the approaches to improve teacher quality through initial teacher education (ITE) in the Canadian province of Alberta, a consistently high-performing system on international comparisons, to the approach taken in the USA, which has consistently fared less well than the average country in these comparisons. Design/methodology/approach – The authors draw on a case study of policies and practices related to teaching and teacher education in Alberta and on analyses of US teaching and teacher education policy to compare a business capital approach with a professional capital approach to ITE. Findings – The decision by philanthropists, business and corporate interests, and the federal government in the USA to invest in the business capital approach has led to the growing privatization of public education. The USA would do well to learn from Alberta’s investment in the professional capital of teachers. Alberta’s system truly is a system that has decided to invest in building “the whole teacher.” The province supports education, including ITE, pays its teachers competitive salaries, and provides access to high quality and teacher-driven professional development. Originality/value – While comparative analyses of education systems are not new, this comparative analysis of ITE in Alberta and the USA using a theoretical framework based on Hargreaves and Fullan’s (2012, 2013) discussion of business and professional capital should give pause to the current US trajectory of disinvesting from university and college-based initial teacher preparation in favor of early-entry programs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".