Scale as an explanatory concept: evaluating Canada's Compassionate Care Benefit
Bibliographic record
Abstract
The concept of ‘scale’ and usage of this term have raised much debate within human geography over the past 25 years. At the same time, these very debates have developed the concept dramatically by offering new considerations of its use. Building on notions that scale is experienced and that scalar concepts offer a vocabulary to articulate complex phenomena, this analysis aims to explore the relevance of scale as an explanatory concept used by informal family caregivers and front‐line health and social care workers when discussing their experiences with a Canadian social programme, the Compassionate Care Benefit (CCB). The goal of the CCB is to provide income assistance and job security to those who take temporary leave from employment to care for a terminally ill family member. As part of a larger evaluative study on the CCB, semi‐structured interviews with 57 family caregivers and 50 front‐line health and social care workers from across Canada were conducted and transcripts were thematically analysed. Emerging from analysis of both datasets was the common usage of scalar concepts, specifically ‘region’, ‘community’ and ‘home’. Respondents employed these scalar categories to reference both differences and relationships in highly spatial and comparative ways, and also to organise and articulate their thoughts in ways meaningful to them and their lives in place. Based upon these scalar categories and issues highlighted by respondents, particular spatial challenges and inequities are illuminated, and implications for the CCB and its administration are identified. These findings provide insight into the complex ways family caregivers and front‐line health and social care workers make sense of their world and more specifically, understand how federal programmes like the CCB operate. By considering how such programmes are experienced in scalar ways, knowledge can be maximised and thus, informed decision‐makers can more effectively meet the needs of programme users.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".