Networks and partnerships in a resource town: A case study of adapting to an aging population in Mackenzie, B.C.
Bibliographic record
Abstract
Adapting to an aging population has become a priority for communities in Canada's resource hinterland, as seniors require specific infrastructure and services to allow them to age-in-place. The ways in which each community responds to these emerging needs is unique and place-specific. In an atmosphere of economic and welfare restructuring, the perceptions of population aging and its implications from those involved in local governance are explored in a case study of Mackenzie, a remote forestry-based community in Northern BC. This study analyzed qualitative data from 33 key informant interviews across the public, private and voluntary sectors between May and June 2005. These data provided valuable insights into three research questions guiding the thesis: how local leaders frame issues of population aging, their perceptions of the allocation of responsibilities for meeting seniors' needs, and experiences and impressions of working together to accomplish collective action. Overall, the interview results suggested that adapting to an aging population in Mackenzie will take time as local leaders are operating in a context of change influenced by economic and social restructuring. What is apparent from this study is that local leaders are well situated to address community issues through their networks and partnerships that draw on social capital and social cohesion when working together.--P.ii.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".