Stakeholder Engagement in an Unconventional Form of Organizing: What Can We Learn from Hackathons?
Bibliographic record
Abstract
The concept of stakeholder engagement has developed around the premise that organizations that involve multiple stakeholders in organizational activities yield better outcomes than those that do not. Considering the importance of stakeholder engagement for addressing grand challenges in a sound and sustainable way, this paper explores how does stakeholder engagement unfold in unconventional form of organizing (UFO) dedicated to creating social value? For doing so, this article studies the stakeholder’s engagement mechanisms of AquaHacking; an initiative that has developed in the field of water conservation in Canada by adopting the organizing form of hackathons. Our analysis shows that in UFOs three mechanisms support the social value creation process: (1) harnessing fluidity, (2) maintaining attractiveness, and (3) ensuring commitment. Our findings also consider the challenges inherent to organizing in unconventional forms and the conditions for the viability of a UFO dedicated to social value creation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".