Building professional capital within a 21st century learning framework
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze current literature on building professional capital, interpreted through the lens of Alberta educators. Through reflections on their field experiences, the authors aim to provide leaders with realistic strategies for developing professional capital, that rely on effective collaborative leadership, professional development (PD), and adult learning. These strategies can be incorporated in a variety of individualized school contexts. Design/methodology/approach Data are interpreted from literature to inform the inquiry into professional capital, focusing on defining effective strategies for attaining professional capital within publicly funded schools in the province of Alberta. Findings Insights are provided for school-based leaders in developing strategies to build professional capital as a means of twenty-first century skill attainment, which includes the transformation from a traditional mindset to innovative teaching and learning practices. Three important elements emerged from the literature review for educational leaders to consider in developing effective professional capital: collaborative leadership, PD, and adult learning. Research limitations/implications Lack of time and funding are most frequently reported as obstacles to implementing professional capital in schools. A number of effective strategies are presented to assist school-based leaders in tackling these hindrances. Originality/value In building professional capital, including human, social, and decisional capital effectively, leaders may embark on incorporating the three focal elements presented in this paper, with an awareness of their staff’s strengths, needs, and pedagogies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".