PUBLIC – PRIVATE AFFAIRS: HOW NONPROFIT ORGANIZATIONS CAN MORE EFFECTIVELY ATTRACT SUPPORT FROM CORPORATIONS
Bibliographic record
Abstract
In its specific focus on the partnerships between nonprofit organizations and corporations from the perspective of the nonprofit organization, this paper adds to the conceptual and theoretical work on the interacting cross-level understanding of social embeddedness. Trust, legitimacy and reciprocity are three important concepts that inform these interorganizational relationships. Hagedoorn’s (2006) three levels of embeddedness: environmental embeddedness, interorganizational embeddedness and dyadic embeddedness provide the theoretical lens through which trust, legitimacy and reciprocity provide prediction regarding the level of support that corporations might offer to nonprofit organizations. Estimated at $241 billion, or two percent of gross domestic product of the United States, philanthropy is big business (Daw, 2006; Gardberg & Fombrun, 2006). Although corporate support represents a relatively small share of total donations (5.6 % in the US and 16 % in Canada), it remains an important contribution (Daw, 2006). In Canada, 43 % of the companies traded on the Toronto Stock Exchange donate between.1 to 2.7 % of their pretax profits (Jantzi Research Associates, 2006). Non-profit organizations, the recipients of these monies and other inkind contributions, play an important role in the economy (Statistics Canada, 2006b); a role that is growing as the sector continues to outpace the economy as a whole (Gardberg & Fombrun, 2006; Statistics Canada, 2006b). This trend is also seen globally as well, with the number of nonprofit organizations continuing to grow steadily (McLaughlin, 2006) and now exceeding 1.5
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".