The power of a simple index of neighbourhood change: Challenging the perception that there is no such thing as simplicity in creating indexes
Bibliographic record
Abstract
While academic research on neighbourhood change has developed over the past 30 years, its measurement is often technical and complex, hampering knowledge translation of academic research to wider audiences. Based on our research on neighbourhood change in Atlantic Canadian cities, we have developed two “simple” indexes of neighbourhood change. We document the steps to creating the simple indexes and discuss what decisions are made along the way. We then compare results of the two indexes with a mean‐centred index that is commonly used for academic audiences. This is done to assess how simpler methods perform compared to one that is considered more sophisticated. Using the 2006 and 2016 Canadian Census data, we apply each of these neighbourhood change indexes to four Atlantic Canadian cities. Results indicate some similarities between the simple indexes and the mean‐centred index. We discuss the methodological and practical implications of the results to help facilitate knowledge translation of academic research for urban planners and NGOs.
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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 | 0.059 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".