Linking Geomatics and Participatory Social Analysis for Environmental Monitoring: Case Studies from Malawi
Bibliographic record
Abstract
In this paper we demonstrate the need for linking geomatics and social analysis methodologies for enumerating and describing land-use change. Until recently, these two methods were used as distinct approaches at varying levels of inquiry. Using two case studies from Malawi, inductive and deductive approaches to land-use/landcover monitoring illustrate the utility of linking geomatics and social analysis as complementary tools. While both studies integrate geomatics with community-based social analysis, the starting point for each is different. The first case study begins by identifying where environmental change is occurring without preconceived hypotheses of causes. The use of geomatics in this case guides the researchers in determining where to conduct more in-depth analysis of explanations of change. The second case study begins with an environmental policy question - "What has been the effect of market liberalization on the environment?" - and then uses remote sensing to evaluate a set of hypotheses. This is the deductive approach. Regardless of the starting point, integrating geomatics and social analysis can promote a cycle of inductive and deductive research. In this way geomatics and social analysis not only address different questions but also can be used to verify and further develop contributions to nature-society dynamics.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".