An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene
Bibliographic record
Abstract
Integrating socio-economic dimensions in ecosystems analysis and management is becoming increasingly important, particularly from a mapping standpoint. A key challenge with such integration is reconciling different geospatial representations based on census and administrative frameworks with natural ecosystems boundaries.This article presents one method for addressing this challenge by mapping an information rich 'ecumene'. In this approach, communities are mapped as human habitats using natural boundaries as opposed to administrative-type boundaries, integrated with authoritative socio-economic data. To illustrate the benefits of this approach, two example applications are provided that: 1) map and estimate the population of the 'forest ecumene' of Canada, and 2) map labour force distribution patterns associated with the forest sector and its relation to forest areas in Canada. Benefits and limitations of this approach are discussed, from which a number of priority areas for future research are identified.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".