Communities of practice and PISA for Schools: Comparative learning or a mode of educational governance?
Bibliographic record
Abstract
This paper examines the Organization for Economic Cooperation and Development’s (OECD) PISA for Schools, a new variant of the Programme for International Student Assessment (PISA) that compares school-level performance on reading, math and science with international schooling systems (e.g., Shanghai-China, Finland). Specifically, I focus here on a professional learning community – the Global Learning Network (GLN) – of U.S. schools and districts that have voluntarily participated in PISA for Schools, and how this, arguably, helps to normatively determine ‘what works’ in education. Drawing suggestively across diverse thinking around contemporary modes of governance, and emerging topological spaces and relations associated with globalization, and informed by interviews with 33 policy actors across the PISA for Schools policy cycle, my analyses suggest that GLN allows the OECD to discursively and normatively constrain how ‘world-class’ schools and systems, and their policies and practices, are defined. However, and in light of the productive capacities of power relations, I also argue that GLN provides opportunities for local educators and leaders to undertake meaningful collaboration and sharing, and to find policy spaces outside of those defined by more performative discursive framings of school accountability. To this end, I explore how GLN may help to foster alternative policy spaces from which educators can ‘talk back’ to national and state authorities, and potentially promote more ‘authentic’ understandings of, and possibilities for, schooling 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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.089 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".