Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› aaag-research-design
aaag-research-design
GitHub针对Annals期刊研究设计的辩护技能，涵盖空间/定量、遥感、定性及混合方法。强调空间依赖、MAUP等核心问题，提供识别策略与同行评审应对方案，强化地理论证与证据的逻辑连接。
Trigger Scenarios
指定识别、抽样或测量方法时
审稿人质疑空间自相关、尺度效应或混淆因素时
需证明设计能区分竞争性解释时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaag-research-design -g -y
SKILL.md
Frontmatter
{
"name": "aaag-research-design",
"description": "Use when defending the research design of an Annals of the American Association of Geographers manuscript — spatial\/quantitative analysis and GIScience, remote-sensing and physical-environmental methods, qualitative human-geography inference, or nature-society mixed methods. The Annals judges each tradition on its own terms. Strengthens the design; it does not write code."
}
Research Design (aaag-research-design)
The Annals spans four areas and accepts many methodologies, but is demanding about each. The design
must credibly connect the geographic argument (aaag-theory-building) to the evidence, and must take
space and scale seriously — spatial dependence, the MAUP, projection, and sampling are design
issues, not afterthoughts. This skill is mode-aware: pick the section that matches your work.
When to trigger
- Specifying identification, sampling, case selection, or measurement
- A reviewer questioned spatial autocorrelation, scale/MAUP, edge effects, validation, or a confound
- Justifying why the design adjudicates the rival account from
aaag-literature-positioning
Spatial / quantitative analysis & GIScience
- Take space seriously. Test and model spatial dependence (Moran's I, spatial lag/error, GWR/MGWR where heterogeneity is the point); state how the MAUP / scale could change conclusions.
- Geography of the data. Document projection/CRS, areal units, edge effects, and the support of measurements; spatial sampling and its biases.
- Inference. Cluster or use spatial SEs at the right level; for spatial autocorrelation, report diagnostics; for prediction, use spatially-aware cross-validation (blocked/spatial CV), not random folds.
Remote sensing / physical-environmental
- Measurement validity. Sensor/resolution choices, atmospheric/geometric correction, and ground truth; quantify accuracy (confusion matrix, kappa/F1, RMSE) with an independent validation sample.
- Process linkage. Tie observed pattern to an earth-surface process and its scale; state the uncertainty budget end to end.
Qualitative / human-geography
- Case selection by design logic (typical, extreme, paired, regional contrast) — say what the case is a case of. Convenience is not a rationale.
- Positionality, reflexivity, and rigor appropriate to the method (ethnography, interviews, archives, discourse/textual analysis); state how interpretations were checked.
- Source/field transparency: plan how fieldnotes, interviews, and archives are documented and cited
(see
aaag-transparency-and-data), including consent and geoprivacy.
Nature-society / mixed methods
- Integrate, don't staple. Specify how the biophysical and social strands inform one another (e.g., land-change observation + livelihood interviews), and how convergence/divergence is handled.
The adjudication test (Annals-specific)
For the single strongest rival explanation, write: "If the rival held rather than my argument, the [spatial pattern / measurements / accounts] would look like ___; instead they look like ___." If the design cannot distinguish them — including ruling out a scale or spatial-autocorrelation artifact — it does not yet identify the contribution.
Referee pushback → Annals-specific fix
| Likely objection | Area | The fix |
|---|---|---|
| "Your OLS ignores spatial autocorrelation." | Methods/Human | Test residual Moran's I; move to a spatial model and report diagnostics. |
| "This is a unit-of-analysis artifact (MAUP)." | Methods/Nature-Society | Re-run across areal units/bandwidths; show stability or scope the claim by scale. |
| "Random CV overstates accuracy on spatial data." | Methods/RS | Use blocked/spatial CV; report the spatial structure of error. |
| "No independent validation of the classification." | RS/Physical | Add a held-out reference sample + area-adjusted accuracy. |
| "Convenience case; what is it a case of?" | Human/Nature-Society | State the case-selection logic and the population it represents. |
| "Whose voice / positionality?" | Human | Make reflexivity and interpretation-checking explicit. |
Calibration anchors
- Space is a design issue, not a covariate. Dependence, scale, projection, and sampling are decided in the design, not patched in robustness.
- Each tradition on its own terms. A qualitative design is not weaker for lacking an estimand; it needs case logic, reflexivity, and disconfirmation criteria instead.
- Mixed means integrated. Two parallel analyses are not mixed methods; specify the linkage.
Anti-patterns
- Ignoring spatial autocorrelation, then reporting OLS SEs as if observations were independent
- No MAUP/scale sensitivity when the result could be a unit-of-analysis artifact
- Classification/prediction with no independent validation, or random CV on spatial data
- Convenience case selection dressed up as theory-driven; positionality omitted in interpretive work
- A nature-society design that never actually links the two strands
Output format
【Mode】spatial-quant / remote-sensing-physical / qualitative / mixed
【Estimand or claim】what is identified/shown
【Spatial integrity】dependence / MAUP-scale / projection / validation handled? [Y/N]
【Rival ruled out】the adjudication sentence (incl. scale/spatial-artifact)
【Robustness】planned checks
【Next】aaag-data-analysis
Supplementary resources
../../resources/external_tools.md— spatial-analysis, GIS, remote-sensing, and CAQDAS tooling by area../../resources/official-source-map.md— areas, scope, and review model
Version History
- 1839142 Current 2026-07-05 12:23
