The rhetorical work of a partnership coordinator in mega-project construction
Bibliographic record
Abstract
Over the past six decades, owners of sport teams and municipalities have been involved in the shared construction of new, state-of-the-art sport facilities. The negotiation and acceptance of these Private–Public Partnerships (PPPs) has often been contentious and unsuccessful. There are, however, other examples where team owners and cities enter into productive business relationships. We argue that one reason for a PPP being successful is that there are certain key actors, partnership coordinators, who are involved in this process. In particular, their skilful “work” at using rhetoric helps characterize the PPP in a specific way whereby the perception is that the risk of the project is distributed evenly amongst all the parties. To explore these ideas, a case study is presented of the successful negotiations in Canada between the Edmonton Oilers of the National Hockey League and the City of Edmonton to build a new downtown arena.
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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 | 0.039 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.043 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".