The Diversity Conflation and Action Ruse: A Critical Discourse Analysis of the OECD’s Framework for Global Competence
Bibliographic record
Abstract
The Organization for Economic Co-operation and Development’s (OECD) Program for International Student Assessment (PISA) 2018 includes a measure of global competence. In PISA, global competence is a cross-curricular domain that aims to measure a set of skills and attitudes that support respectful relationships with people from different cultural backgrounds and engage for peaceful and sustainable societies. This paper builds theoretically and empirically from previous research that investigates the framing and messaging of global education policy as well as the tendency to conflate local and global approaches to diversity and difference in research and practice. We critically explore the OECD’s framework of global competence in PISA 2018 by reporting on two key findings from a critical discourse analysis. We examine language use and discursive practices to consider how global competence in the OECD 2018 framework document is structured, messaged, and mediated at an international level, and to what extent it reflects critiques around individualization and conflation of multiculturalism and global citizenship. We organized findings on two major themes, namely encountering the “other” and taking action.
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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 | 0.049 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.017 | 0.076 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".