Space, Time, and Local Employment Growth: An Application of Spatial Regression Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Local and regional employment growth is generally studied either by searching for local qualitative explanatory factors such as governance, synergy between firms, and milieu effects, or by searching for general growth factors using statistical techniques. The body of work that relies on this approach has tended, in keeping with economics’ nomothetic tradition, to assume that local and regional growth factors are constant over space. The focus of this paper is on exploring the spatial stationarity of employment growth factors in Canada, but it also seeks to clarify some of the broad principles behind spatial regression techniques in order to provide a point of entry and a conceptual framework for empirical researchers. To do so, we apply a recently developed technique, Geographically Weighted Regression (GWR), and we explore the method's advantages and limits for answering our research question. We find evidence that growth factors differ across Canada, but we also conclude that the GWR technique, given the number and shape of regions available for our analysis and given certain limitations that are currently inherent to the method, can only provide tentative and exploratory results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it