Bibliographic record
Abstract
You Responded, We Listened I n November, the DC Board and Management Team met to distil key drivers, information and data as a basis of environmental scanning using the Board-facilitated member/nonmember survey (totaling 2093 respondents), Global Nutrition Report, National Workforce Data Report, Canadian Medical Association (CMA) quarterly E-scans, DC Vision 2020, and input from the Canadian Association of Professional Programs in Human Nutrition (CAPPHN).With the expert leadership and facilitation of the Consultant, Rose Mercier of The Governance Coach, driving forces were lifted from the identified documents, enabling the process of shaping the future context and spaces in which DC operates.Ultimately, we discussed DC's preferred future.Developing a capacity for strategic foresight is important for the Board, enabling us to set direction that enhances the likelihood of continuing organizational relevance.We live in a complex world; therefore, an important Board role is to continue to identify factors that might impact DC, as well as how those factors may interact with each other.By engaging in ongoing environmental scanning, the Board strives to avoid being caught unaware of game-changing factors.Subsequent to the Board/Management Team meeting, the Board continued a dedicated process of developing a new set of Ends-the result the organization is to produce.The newly developed Ends will be a component of discussion during the Member Dialogue at the June 2016 Annual DC Conference in Winnipeg.Your responses to the survey in November helped the Board determine what specific outcomes and impacts will bring the greatest value to Association members.This greatly impacted our discussion regarding the Association's preferred future and Ends development!You Responded, We Listened!(
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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 | 0.019 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.040 | 0.066 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".