Communities of practice and PISA for Schools: Comparative learning or a mode of educational governance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it