Impact of Senior Government Policies on the Renewal of Built Capital for Rural Non-Profits
Bibliographic record
Abstract
Changes in rural service provision have been shaped by a shift from state to private and nonprofit service delivery, driven by a neo-liberal policy orientation. As senior governments continue to offload the delivery of services to nonprofit stakeholders in rural regions, state policies often fail to address deficiencies with aging and inappropriate built capital assets. The infrastructure and service deficit undermines the capacity of the nonprofit sector, and threatens the overall resilience of rural communities. At the community level, nonprofits have been pursuing new institutional/structural arrangements and exploring opportunities to address infrastructure deficiencies in order to strengthen their resilience within neo-liberal public policy approaches. Building upon 51 key informant interviews in 35 small communities in British Columbia, Canada, our research addresses gaps in understanding how senior government policies are developing conditions necessary to support the renewal of infrastructure assets in the nonprofit sector. Our findings suggest that senior government policy and funding structures may not provide the conditions necessary to enable nonprofits to renew their built capital assets that could strengthen the long-term viability of rural service provision. These findings reinforce the need for post COVID-19, or similar recession response stimulus, initiatives to include wise infrastructure investments that enhance the capacity and efficiency of rural nonprofit service providers.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".