Developing professional capital in teaching through initial teacher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it