Bibliographic record
Abstract
The development of preschool systems and the expansion of leadership roles within these systems is evidence of a significant shift in how the early childhood field is being recognized. However, little is known about who is leading preschool system building efforts during this time of unprecedented change, as there is limited empirical research on state early education leaders. This mixed methods study explored three main research questions: Who are state early education leaders and how did they get there? How do state early education leaders describe their work as system leaders? How do state early education leaders define and describe leading at the state level? Quantitative data was gathered using an electronic survey that was distributed to the population of state early education leaders (n=140), resulting in 89 survey respondents. Qualitative methods were then used to better understand the quantitative findings (Remler & van Ryzin, 2011) and gather first-hand accounts (Hardin, 1987) and in-depth descriptions of state ECE leaders’ work and experiences through two semi-structured interviews. This study’s findings describe the demographics of state early education leaders and used leaders’ experiences working in the field to identify and map the most common pathways into early education leadership. Leaders work included developing and communicating visions for early education in their states and creating policies and systems to unify early childhood services and early education offerings with the K-12 system. However, leaders reported that the fragmentation of early childhood services and the limited authority they were given in their positions meant they could only engage in system building at a superficial level. Finally, leaders described how working in a female-dominated field and the positioning of early childhood education as “less than” K-12 influenced their behavior as leaders. This study begins to build a more robust research base on state systems leaders and provides important insights into what types of preparation and professional development support these leaders need.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.147 | 0.046 |
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".