Selective Adoption or Comprehensive Learning? Domestic Policy Makers’ Use of International Organization and Global Management Consulting Firm Advice in Future Skills Policy Making in Canada
Bibliographic record
Abstract
The fourth industrial revolution, brought about by technological innovations including artificial intelligence, automation, and advanced robotics, is already shaping many economies around the world. International Organizations (IOs) and Global Management Consulting Firms (GMCFs) are key sources of information for how domestic economies can respond to these anticipated disruptions and “future proof” their economies. But do domestic policy makers pay attention to IO and GMCF advice to inform policy making and to what extent are their ideas considered authoritative and influence domestic policy agendas? This article examines these organizations’ informational and agenda setting power in domestic policy formulation, focusing on the case of Canada’s future skills policy making community. Using qualitative research methods including thematic analysis of 26 interviews within the policy community and citation analysis of policy documents, the study reveals mixed findings. IOs and GMCFs were important sources of information, among many, for domestic policy actors in the future skills policy community. Contrary to the expectation of selective uptake of their advice based on their perceived authoritativeness, however, we find much more evidence of more comprehensive learning amongst domestic policy actors, with variation observed based on the domestic actors’ roles within their organizations.
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.
How this classification was reachedexpand
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".