‘Left Behind Places’: Examining the Evolution of a Concept With an Application to the Canadian Regional Development Context
Bibliographic record
Abstract
ABSTRACT ‘Left‐behind places’ (LBPs) are generally defined as places experiencing economic stagnation and decline, typically reflected in post‐industrial regions and rural areas. In recent years, the concept has gained increased popularity within urban and regional studies in the United Kingdom (UK) and other European countries. Research on the topic in the Canadian context, however, remains limited, with few studies specifically discussing how LBPs are defined in Canada, and even fewer attempting to empirically assess where they may be located across the country. The paper's objectives are twofold: (i) it examines the evolution of the concept of LBPs through an extensive literature review and (ii) explores its application to Canada. The paper employs Statistics Canada's 2021 Canadian Index of Multiple Deprivation (CIMD) dataset as a proxy for identifying LBPs across the country and examines their spatial dynamics at the census subdivision (CSD) level using local indicators of spatial association (LISA) statistics. A multinomial logistic regression model is also developed to explore regional factors. The analysis finds high clustering of CSDs with high levels of deprivation in rural, northern, and remote areas of Canada, as well as high clustering of CSDs with high levels of economic dependency in and around major Canadian cities.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".