Searching for Substance: Externalization, Politicization and the Work of Canadian Policy Consultants 2006-2013
Bibliographic record
Abstract
The nature of policy advisory systems and the capacity and influence of individual system actors has been a subject of much interest in recent years, especially vis-à-vis observed trends towards the twin themes of politicization and externalization of policy advice. Studies to date for the most part have focused only on the capacity of highly visible advisory system actors such as professional policy analysts in government or those in the NGO and business sectors. This study examines the role of the ‘shadow’ or ‘invisible’ actors employed by governments on temporary contracts as managerial or other kinds of policy consultants to undertake activities related to policy development and evaluation processes. The study reports on the findings of a 2012-2013 survey of such consultants in Canada and presents data on relevant aspects of their background, training, perceptions and capabilities compared to permanent policy analysts employed fulltime by governments. It finds most consultants to be better qualified than their permanent counterparts and to primarily engage, like the latter, in process-related policy work. This answers some questions about the roles and relationships of these members of the advisory system but raises other questions about where the ‘substance’ of policies originates.
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 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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".