Sponsorship Communication Strategy for the Asthma Society of Canada: Implications for Nonprofit Organizations in Bangladesh
Bibliographic record
Abstract
The Seja’s Run is a public awareness building program conducted by the Asthma Society of Canada and the Alumni Association of the Toronto French School for the last 18 years. The empirical evidences have shown that the Seja’s Run event can be expanded beyond its present limited audiences and community. The secondary analysis, in this paper, on the non-profit industry in Canada has demonstrated that corporations are significantly contributing to non-profit sector in the form of grants, donation, in-kind supports and sponsorships. However, non-profit organizations need to come up with well-designed communication strategy for soliciting and thereby convincing corporations for sponsorships. Over the previous years, the Seja’s Run got sponsorship mostly through personal relationships and contacts of the people involved in the event committee. After evaluating the present scopes and future potentials of the Seja’s Run, five major current donors of the event were interviewed regarding the event. Based on the comments and feedbacks from these donors, this paper also presents a guideline for designing sponsorship communication strategy for the non-profit organizations in Bangladesh.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".