Professional Capital as Accountability
Bibliographic record
Abstract
This paper seeks to clarify and spells out the responsibilities of policy makers to create the conditions for an effective accountability system that produces substantial improvements in student learning, strengthens the teaching profession, and provides transparency of results to the public. The authors point out that U.S. policy makers will need to make a major shift from a heavy reliance on external accountability and superficial structural solutions (e.g., professional standards of practice) to investing in and building the professional capital of all teachers and leaders throughout the system. The article draws key lessons from highly effective school systems in the United States and internationally to argue that the priority for policy makers should be to lead with creating the conditions for internal accountability, that is, the collective responsibility within the teaching profession for the continuous improvement and success of all students. This approach is based on the development and circulation of professional capital that consists of three components: individual human capital, social capital (where teachers learn from each other), and decisional capital (developing judgment and expertise over time). In this new professional accountability model, the external accountability that reassures the public that the system is performing in line with societal expectations continues to be an important role of educational systems, but it is nurtured and sustained by the development of strong internal accountability.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".