Bibliographic record
Abstract
This presentation will use the example of the Regional Economic Development Alliances (REDAs) in Alberta to present lessons for leading and governing collaborative networks. REDAs were created in 1998 and continue to today. Always positioned as an initiative by the Government of Alberta (GOA), REDAs were never established as a program. This has significant implications for leadership and for funding. From 1998 through 2009, government staff were managers of each REDA, accountable to regional governance boards. In 2011 the GOA withdrew staffing and infrastructure support, turning to the governance boards to determine the futures of each REDA. This shift had consequences, intentional and unintentional, which continue to impact REDA capacity and challenge their viability. In 2014, the REDAs developed a model for renewal, which was endorsed by the GOA. This created increased project funding but no adjustment to operational assistance, as was recommended. Amid this dynamic environment, REDAs have experienced success, although overall impact has been uneven. The presenter has worked with REDAs since 1998 as a management board member, a government employee managing two REDAs, an external party forming strategic alliances with REDAs, the consultant hired to complete the REDA renewal process and now managing a REDA using a suite-of-services contract model. In 2013, Mrs. Goulet completed a MA-IS (AU) and based much of her graduate work around the role of leadership in rural economy. Her knowledge of regional economic collaborative structures is both broad and intimate. Topics covered will an overview of the REDA initiative from 1998 to present; the benefits and drawbacks of each stage of evolution; and the role and regional power dynamic of governance boards and the pivotal role of management. The presentation will close with observations on leading regional economic alliances and suggestions for the way forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".