Building legitimacy at Sport Canada: pitfalls of public value creation?
Bibliographic record
Abstract
Legitimacy building is an increasingly recognized feature of bureaucratic practice under networked governance. Mark Moore's (1995) model of ‘public value creation’ captures the strategic imperative to build credibility, legitimacy and support for policies and programmes in this context. As an emerging ‘narrative’ in public organizations, it combines perspectives on managerial and political leadership and is therefore useful to explain bureaucratic experiences with legitimacy building and their implications for policy. This article investigates the efforts of Canada's federal sport agency to secure legitimacy and explores the implications of public value creation on future policy-making. Drawing from interview data as well as public and internal documents, the study outlines Sport Canada's initiatives to establish sport's ‘benefits’, collaborate widely and add value in terms of equity and ethics. Two related implications (or storylines) are considered and discussed: (a) the tendency for public value creation to reduce the policy sector to anecdotal understandings in the eyes of political authorizers, and (b) the propensity for building public value to result in over-commitment due to the accumulation and embedding of new collaborations, mandates and target populations. Points for practitioners Mark Moore's ‘public value’ approach resonates with practitioners because of its realist portrayal of politics in public administration. Generating support and legitimacy for existing or proposed policies and programmes is acknowledged to be particularly important in contexts where networks are prevalent. This article investigates legitimacy building within federal sport, a sector which has been reliant on networked governance since its inception and where substantive efforts have been aimed at raising its profile. The case cautions against persistent case-building and public value creation because continuous justifications may ultimately raise questions regarding the credibility of bureaucratic advice and of the likely success of programme commitments.
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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.054 | 0.068 |
| Scholarly communication | 0.037 | 0.017 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".