The Future Challenges of Cross Sector Interactions: Interaction between NonProfit Organisations and Businesses
Bibliographic record
Abstract
The paper aims to offer a starting point of a future research agenda on Cross Sector Social Partnerships that will be informed by both theory and practice, addressing the challenges that both business and nonprofit organisations will face due to and as a result of their increased interactions. In order to articulate the need for both organisational and social change through cross sector social partnerships the paper suggests that it is required to move towards multidimensional levels of analysis within multiple contexts that will emphasise a historical perspective rather than an ahistorical analysis of events outside of their context. Hence four categories are proposed in order to group a future research agenda: context, process, content and impacts. By extending the three levels of Pettigrew’s analysis of change the paper suggests that there is a need to include a fourth category that refers to the impacts/consequences of interactions. If indeed partnerships are able to facilitate change within their context but also in their external environment then we need to similarly study their impacts. The paper offers research suggestions under each of the four proposed categories and also on methodological issues within partnerships research.
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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".