Assessing the Collaboration That Was “Collaborative Federalism” 1996-2006
Bibliographic record
Abstract
From a vantage point fifteen to twenty years after a number of scholars labeled the intergovernmental climate of the mid/late 1990s as "collaborative federalism," this article re-assess the appropriateness of this label. Looking particularly at social policy, we consider the process of col- laboration itself, both in terms of the institutions and forums where the federal and provincial partners to the collabora- tion met (have initial attempts to grow the apparatus of intergovernmental negotiations had lasting effects), and in terms of the culture and relationships involved (have prov- inces and the federal government negotiated in ways that place the two orders of government on equal footing, or have they reverted to a hierarchical relationship). The article also considers whether provincial and federal governments pro- duced collaborative policy outcomes, given their pledges to do so, as elaborated in a series of intergovernmental agree- ments. We find that the “collaborative” of collaborative federalism comes to look quite thin, particularly compared to the definition of collaboration advanced by scholars a decade ago. We conclude with some brief reflections on what the lack of collaboration in collaborative federalism means for the broader taxonomic question of how we understand the intergovernmental relations of these years, and suggest that a more accurate descriptor might be the unraveling of competitive federalism.
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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 | 0.048 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".