A critical analysis of international organizations’ and global management consulting firms’ consensus around twenty-first century skills
Bibliographic record
Abstract
A growing number of academic studies and policy reports have identified a set of core skills considered crucial in the twenty-first century economy. This article critically examines the evidence base underpinning that ideational consensus among international organizations (IOs) and global management consulting firms (GMCFs). We collected 234 skills reports produced over the past decade by major IOs (European Commission, ILO, OECD, UNESCO, and World Bank) and GMCFs (BCG, Deloitte, Ernest and Young, KPMG, McKinsey, and PWC). We then extracted bibliographic references from each report and used the analytic technique of citation analysis to examine how the consensus around these core skills was generated in order to uncover the authoritative sources of knowledge and the pattern of ideational policy diffusion observed. Our analysis reveals substantial gaps in the evidence base used. Evidence drew largely on a few academic economists, along with strong use of grey literature, and high rates of self-citation. Given these characteristics, the consensus around twenty-first century skills appears less epistemic in nature and more like an ideational echo chamber, which raises concerns about the extent to which policymakers should rely on this evidence.
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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 | 0.055 | 0.239 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.087 | 0.096 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".