The diminished invisible private service: consultants and public policy in Canada
Bibliographic record
Abstract
Introduction In the last ten years, the invisible private service has become increasingly visible in both the academic literature and in the media. Numerous scholars have studied policy, planning, and management consultants to better understand what type of consulting is being conducted for the different orders of government, how much money is being spent on consultants, and the extent of influence on public policy and processes. In a similar vein, the media have increasingly commented on public sector consulting projects given high-profile projects and issues. For example, revelations from the Gomery Commission (2005) about contracts were in the national media for months, and disclosures about the Harper government hiring Deloitte for almost $90,000 per day (total contract $19.8 million) to assist the federal cabinet in identifying cost-saving initiatives triggered many journalists to question the need for such a task (Beeby, 2011). The Toronto Star , in an extensive investigative report, found that “90 percent of the $2.4 billion paid out for management consulting in the past decade comes with no description of the work done on the government's public disclosure sites” (McLean and Bailey, 2013). What was an essentially invisible private service a decade ago is slowly gaining recognition, at least at the academic level—and to a certain extent in media circles and the public at large. Yet, it is still challenging to get meaningful data from any order of government or from the management consultants that have been engaged in consulting activities with the public sector. Despite the billions of dollars spent on consultants in the public sector in Canada (McLean and Bailey, 2013), there has been little attention paid to this elusive unregulated policy actor (CMC, 2016a). The purpose of this chapter is to introduce the reader to the role of the consulting industry for those who are not familiar with this non-state actor. Despite recent scholarly attention to this topic, the role of consultants in the public sector is still neither well understood nor well defined (Buono, 2001; Howlett & Migone, 2013a, 2013d, 2014a, 2014b), especially from a Canadian perspective (Saint-Martin, 2000; Bakvis, 1997; Meredith & Martin, 1970; Howlett & Migone, 2013a, 2014a, 2014b; Howlett et al., 2014).
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".