Project blue sky : a case study
Bibliographic record
Abstract
The objective of Project Blue Sky was to mobilize Olympic and Paralympic athletes to take a leadership role and to use the Vancouver 2010 Olympic and Paralympic Winter Games to shine a spotlight on climate change. To achieve this goal, spokespeople were identified, partnerships were formed, social media was activated, blog seeding was planned, and different media outlets were approached. The website attracted environmentally, socially, and health conscious individuals because of its innovative and original concept. A website needs to be flexible and go through many iterations before it’s complete. Unfortunately, many of the changes that were applied to the Project Blue Sky website were not done early enough in order to make a substantial difference towards its 1 billion kilometre goal. This report will discuss the methods that Project Blue Sky used to attract members to the website and will also discuss what the barriers to engagement were. In addition, this report will discuss how the website started, who was involved in the project and what the Project Blue Sky team learned throughout the project. The team at Project Blue Sky hopes this report will serve as a tool for future community engagement projects. According to a report from the David Suzuki Foundation, “(public engagement) is the category where VANOC (has) had the least success”i. As such, this report aims to understand how engagement, or lack there of, contributed to the limited success of this project and will offer recommendations for future projects. The recommendations found throughout this report are summarized in Appendix 1.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".