Bibliographic record
Abstract
student assessments, and student support data. Two questions helped us to use the data productively: What patterns are showing up in the data? What little things can we do to address the concerns of students? These questions assured staff that our focus was where it needed to be – not on evaluating individual teachers, but on what we might do as a team to set some new conditions for learning. In September 2008, I made adjustments to my own practice and introduced the What did you do in school today? project early to new staff. I listed everything I had done the previous year to develop a team approach to decision-making and then presented data from a teacher’s survey, indicating that teachers clearly did not yet feel that they were part of a team or involved in shared decision making. I then asked the staff to describe what teamwork and shared decision-making meant to them. Their responses provided a focus for our ongoing development as a professional learning community. Figure 1 shows the agenda we followed at one transformational school-based professional development day. For the past six years, the Halifax Regional School Board has been developing a solid framework for improving student achievement. As a school principal, I welcomed the board’s direction because it resonated with my own beliefs about our purpose as educators and our need to use data effectively. The Canadian Education Association’s (CEA) initiative, What did you do in school today? is about reflecting on thoughtful questions and using data to make improvements.1 It has played a significant role in creating positive change in our school over the past two years, but it didn’t happen on its own, and it didn’t happen overnight. Before our school could make those improvements, we had to get comfortable with data. When I started as principal at Sir Robert Borden Junior High School in 2007, I set ambitious goals for the school, inspired by what I believe about evidence-based decision making, professional learning, teamwork, and the importance of measuring everything we do as a staff by its impact on student learning. However, I could see at the end of our first day together that the staff was completely overwhelmed; they weren’t yet comfortable using data and clearly felt it might be used to judge them. Just as we traditionally have blamed students when they struggle to succeed in school, I attributed the fear and resistance to the culture of the school, not realizing that I had forged ahead as the hare, when I should have taken the slow and steady path of the tortoise. I had to pause and try to see my own ideas from the perspective of the staff. What context had I provided? What connections had I made to help us all see the relationships among the many different initiatives that seem to continually come at us at high speed? What coaching had I provided to foster the skills of data analysis? I took a big step back and a few small steps forward. Working as a team, we developed shared answers to these questions, and we started to explore the value of teamwork for school and classroom improvement. We began asking the right questions and effectively using the answers to guide our classroom practice. And so, when our school was given the opportunity to participate in the What did you do in school today? survey, we were ready. Matthew Moriarty, a teacher at SRB, took on the role of survey coordinator.
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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 | 0.381 | 0.558 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.043 | 0.045 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".