Regulating the wild west? Management consulting reform in Canadian government
Bibliographic record
Abstract
Abstract Globally, the increased reliance on consultants and contract employees has raised many concerns vis‐à‐vis more traditional “in‐house” provision. One such concern is the manner in which consultants have been displacing traditional advisors in important aspects of government business while others raise issues with the quality of services rendered and the (undue) influence of the advice tendered. This trend has fuelled research in political science, management, public administration, and public policy chronicling the possible rise of a “consultocracy” and leading to calls in many countries for better regulation of consultants, who currently exist almost everywhere in an unregulated “wild west.” We examine the Canadian situation, the current lack of regulation of policy and management consultants, and prospects for reform, drawing on contract data, interviews with consultants and government employers as well as comparisons with the situation in other countries like the UK. We argue that establishing a more professional licensing and credentialing system for the consulting industry through a regulatory intermediary would improve the efficiency of its services and allay many of the concerns raised.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".