Understanding the Role of Social Capital in Government Collaboration on Climate Change: Evidence from New York
Bibliographic record
Abstract
Natural resource and environmental agencies are charged with transferring and integrating science based knowledge across institutional boundaries so that they can work together to address environmental challenges. Unfortunately, their hierarchical organizational design is not well suited to this task, particularly in relation to horizontal transfer and integration of knowledge and responsiveness to change. In recognition of this, government has supported the development of non hierarchical mechanisms such as “collaborative networks” and “boundary-less” organizations. However, in order to be effective, such mechanisms must be reinforced by sufficient social capital to enable the people and organizations participating in them to meet the accountability and efficiency requirements of the hierarchical bureaucracy while providing them with the flexibility they need to collaboratively respond to changing problems and tasks. Social capital is the advantage that an individual or group receives from features of social relationships such as trust, networks, and norms. Presently, there is little empirical evidence available on the patterns of social capital that exist among natural resource and environmental agencies in government and the practical opportunities and constraints that they present for enhancing the transfer and integration of science based knowledge across institutional boundaries. In the absence of such an understanding, dynamics that critically affect the capacity of agencies to effectively respond to complex, multi-scalar, and cross-cutting environmental issues are likely to go unidentified and unmanaged at a sufficiently strategic level within government. Thus, there are likely to be significant gaps between the potential and actual capacity of public sector agencies to collaborate in ways that enable them to effectively draw on science-based knowledge to develop innovative and integrated responses to dynamic environmental challenges, a good example of which is climate change. Given this, our research objectives are as follows: (1) measure the social capital that is present among staff in government agencies charged with working together to address a climate change in New York State; (2) measure the transfer of scientific knowledge among staff in the selected agencies; (3) map the patterns of social capital and the transfer of science-based knowledge among these agencies and examine the relationships between them; and (4) identify strategies for enhancing the transfer of science-based knowledge that are sensitive to both the accountability requirements of government agencies and the need to be responsive to changing problems and tasks. For this study we utilize two sources of data: (1) an online survey, distributed to roughly two hundred civil servants in state and municipal public agencies, with multiple choice questions aimed at measuring the amount of social capital and trust present in the system; and (2) in-depth semi-structured interviews with approximately thirty public agency employees who are involved in climate change governance in their professional roles. The survey data contains enough cases to establish the validity of our findings, while the interviews establish the reliability of the inferences drawn from the survey data. Our findings will provide insights into the relationships between social capital and the transfer of science-based knowledge among the agencies surveyed and the implications of these relationships. The findings will also provide practical insights that will have relevance to natural resource and environment agencies more broadly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".