From clipboards to annual reports: innovations in sport for development fact management
Bibliographic record
Abstract
Purpose: This paper examines relationships between fact management and innovation. How and why a sport for development agency contributes to social innovation by transforming data into accounting assets.Design/methodology/approach: Actor-Network Theory is used to retrace how and why the managers at Special Olympics Canada innovate as they craft new annual reports.Findings: Today, most non-profit organizations face increased pressure to better evaluate and account for their mission attainment. We propose that process and organizational innovations are required for effective and efficient fact management, and that effective fact management contributes to social innovations. We submit that translating data into presentable facts involves innovating Collecting, Connecting, Collating and Communicating efforts.Practical implications/Research Contribution: Innovation in the field of sport for development has received less attention. The proposed model is one of the first conceptualizations of how, and why, qualified facts concerning lives enriched through sport are built. Its practical and theoretical contributions to social innovations are also discussed.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".