方法论指南
我该使用哪种分析？
Lacuna 是建立在同一研究记忆之上的三个引擎，再加上以另一家族重新阅读同一框架的综合桥接。从你的问题出发，合适的家族自然随之而来。
从你的问题出发
谁在塑造这一领域，沿着哪些轴线？
文献计量
文献说了什么 — 又有什么没有说出口？
计算主题
汇总证据有多强，决策有多可辩护？
元分析
我有一个框架，想用另一种视角来阅读它。
综合桥接
三个引擎与综合桥接
- 文献计量6 种方法
将引用、合著、关键词与时间流转化为一张研究地图 — 领域在何处密集、影响力中心是谁，以及桥接与断裂位于何处。
当你的问题关乎某一领域的结构而非单一效应时使用。
- 计算主题6 种方法
在语料文本中显现概念邻近性、重复与沉默地带。它是计算式的 — 并非 Braun–Clarke 反思性主题分析。
用于绘制主题、追踪其演变，并以文本证据标注候选空白。
- 元分析12 种方法
将元分析从计算器输出中取出，绑定到一条决策轨迹：效应量、异质性、模型选择与来源。
当你有一张效应表并需要可辩护的汇总估计时使用。
- 综合桥接4 种方法
对同一框架的交叉阅读：每座桥接通过另一家族的运行重新阅读所附效应表 — 时间、主题、依赖或引用。
在基础运行之后使用，以第二种视角阅读一组证据。
完整方法注册表
下面是按家族分组的每种方法的公开摘要。包含每条规则的辩护文本与来源的完整规则注册表位于工作区内。
文献计量
Bibliometric Overview
Headline descriptives: production, growth, top sources, authors and keywords.
引擎: Lacuna native (Python)
Sources Analysis
Source productivity and core journals via Bradford’s Law zones.
引擎: Lacuna native (Python)
Authors Analysis
Author productivity (Lotka’s Law), impact and dominance over time.
引擎: Lacuna native (Python)
Documents Analysis
Most-cited documents and the words that travel with them.
引擎: Lacuna native (Python)
Intellectual Structure
Co-citation and bibliographic coupling — the intellectual base and research fronts.
引擎: Lacuna native (Python)
Social Structure
Collaboration networks across authors, institutions and countries.
引擎: Lacuna native (Python)
计算主题
Co-word Analysis & Thematic Map
Keyword co-occurrence clustered into a strategic diagram (centrality × density).
引擎: Lacuna native (Python)
Topic Modeling (LDA / NMF)
Probabilistic or matrix-factorisation topics over the full-text corpus.
引擎: Lacuna native (Python)
Embedding-based Topic Clustering
Documents embedded (TF-IDF by default; transformer/SBERT optional) and clustered into semantically coherent topics.
引擎: Lacuna native (Python)
Factorial Analysis
MCA / correspondence analysis projecting terms into a conceptual map.
引擎: Lacuna native (Python)
Thematic Evolution
How themes split, merge and flow across time slices (Sankey).
引擎: Lacuna native (Python)
Trending Topics & Burst Detection
Emerging terms and citation bursts over the publication timeline.
引擎: Lacuna native (Python)
元分析
Univariate Meta-Analysis
Pool one effect size per study into a single summary estimate with heterogeneity diagnostics.
引擎: metafor 5.0.1 (R)
Multilevel Meta-Analysis
Two- or three-level models for dependent effect sizes nested within studies or labs.
引擎: metafor 5.0.1 (R)
GLMM Meta-Analysis
Generalised linear mixed models for binary/count outcomes without normal approximation.
引擎: metafor 5.0.1 (R)
Network Meta-Analysis
Mixed treatment comparison across ≥3 interventions with a connected evidence network.
引擎: netmeta (R)
Diagnostic Test Accuracy
Bivariate sensitivity/specificity model with a summary ROC curve.
引擎: mada (R)
Dose–Response Meta-Analysis
Model the shape of an exposure–outcome relationship across dose levels.
引擎: dosresmeta 2.2.0 (R)
P-Uniform*
Bias-corrected effect estimate robust to selective publication.
引擎: Lacuna native (Python)
P-Curve Analysis
Test the significant p-values for evidential value vs p-hacking via the p-curve's right-skew (Simonsohn et al. 2014).
引擎: Lacuna native (Python)
Trial Sequential Analysis
Adjust cumulative meta-analysis for repeated significance testing — is the evidence conclusive, or is more information needed (Wetterslev et al. 2008)?
引擎: Lacuna native (Python)
MA Power Analysis
Prospective / retrospective power for a meta-analytic design specification.
引擎: Lacuna native (Python)
综合桥接
Cumulative Reading (Time Bridge)
Re-estimate the pooled effect after each study in year order — when the evidence first became conclusive.
引擎: Lacuna native (Python)
Theme Subgroup Reading (Topic-Model Bridge)
Re-pool as a subgroup analysis whose grouping variable is each study's theme, taken from a topic-model run.
引擎: Lacuna native (Python)
Dependency Reading (Author-Network Bridge)
Re-pool with cluster-robust variance for studies produced by the same research team, from an author-network run.
引擎: Lacuna native (Python)
Evidence-Base Diagnosis (Citation Bridge)
Read the body of evidence behind a meta-analysis descriptively, matched to the project's bibliographic records.
引擎: Lacuna native (Python)
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