How to progress on sustainable development through Public-Private Partnerships
Bibliographic record
Abstract
With the expiration of the Millennium Development Goals (MDGs) in 2015, the United\nNations (UN) has called for a collective effort to create a global operational framework that\nenables key actors to develop long-term strategies for sustainable development. At the same\ntime, there has been an increase in the interest in public-private partnerships (PPPs) and their\npotential to make social, environmental and economic improvements and thereby create\nsustainable structural changes. In Sweden, the Swedish International Development\nCooperation Agency (SIDA) has initiated a collaboration with 20 Swedish-rooted firms that\ntogether form the network “Swedish Leadership for Sustainable Development” (SLSD). Two\nof the partnership’s ambitious objectives include creating decent jobs and fighting corruption\n– two factors which are regarded to have a profound impact on poverty reduction and\nsustainable development.\nTo better understand how a PPP of this kind can operate effectively, this study aimed at\nexploring and identifying what success factors need to be in place for successfully\ncooperating on making a contribution to sustainable development through the creation of\ndecent jobs and decreasing corruption. To address this issue, a qualitative approach was taken\nwhere data were collected from a multiple case study of two companies involved in the SLSD\npartnership, Volvo and Tetra Laval, and their previous experiences of cooperating with SIDA\non issues related to development.\nBased on the results of the study, four factors that could enable a PPP to successfully\ncontribute to sustainable development through decent job creation and fighting corruption\nwere identified. These are: 1) combine competencies to adapt the initiative to the specific\ncontext; 2) design the initiative in a way that enables it to contribute to structural changes; 3)\nthe initiative should relate to the private partner’s core operations to ensure that competencies\nare used to the fullest; and 4) it should generate added value for all stakeholders involved.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.678 | 0.020 |
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; both teacher heads agree on what is shown here.
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".