<i>Harambee!</i> A Triadic Perspective on Social Impact: Organizations, Evaluators, and Target Beneficiaries in Kenya
Bibliographic record
Abstract
Organizations often claim that their actions benefit others, for example in social impact initiatives, eliciting positive legitimacy evaluations from a broad range of audiences even though such initiatives may produce limited or even harmful effects on target beneficiaries. While scholars have begun to examine relational dynamics between organizations and evaluators who render judgments about organizational legitimacy, target beneficiaries have been typically considered as the passive recipients of positive or negative impacts of organizational actions. Drawing on qualitative data from a corporate social responsibility project in Kenya, this study reveals a triadic relationship (organization–evaluators–target beneficiaries) that establishes organizational legitimacy in the eyes of evaluators while generating substantive benefits for target beneficiaries. Far from being passive, target beneficiaries actively participated in the organizational legitimation process by corroborating, in their communications with evaluators, the organization’s social impact claims. This corroboration provided leverage for the target beneficiaries to negotiate organizational support in order for them to redirect off-the-shelf practices toward contextualized practices that generated substantive benefits to themselves. Going beyond the organization–evaluator dyad, the study contributes a triadic perspective on social impact and reveals how target beneficiaries’ participation can reshape the processes and outcomes of social impact creation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".