技能 数据科学 学术论文文献定位指南

学术论文文献定位指南

v20260724
restud-literature-positioning
本技能指导用户如何撰写高质量的学术论文文献定位部分。它教用户如何避免简单的文献罗列,而是通过系统性地与最接近的现有工作进行辩论,清晰界定其边际贡献,并阐明研究的必要性和时代背景,从而确保文章的创新性和可读性。
获取技能
399 次下载
概览

REStud Literature Positioning (restud-literature-positioning)

When to trigger

  • The related-work discussion is a list of citations rather than an argument
  • The user cannot name the single closest paper and what theirs adds
  • A referee or seminar comment was "how is this different from [X]?"
  • The contribution is original (per restud-topic-selection) but its location in the literature is unclear

What REStud positioning must do

REStud is general-interest and weights theory and applied work equally, so the positioning has two jobs at once:

  1. Confront the closest papers honestly. Name the 3–5 nearest results and state precisely what each one already established and what it could not.
  2. Make the marginal contribution legible to a non-specialist on either side. Because the handling editor and referees may sit on the theory or the applied side (the Joint Managing Editors span IO/applied econometrics, micro theory, and information economics), an applied paper should be legible to a theorist and a theory paper to an applied reader. Models that defined whole literatures appeared here first — Mirrlees (1971) on optimal taxation, Stiglitz (1974) on sharecropping incentives — so the register is "this changes how the field models X," not "this adds new data to X."

A REStud-quality positioning paragraph is an argument, not a literature dump: "Paper A established X but assumed Y; paper B relaxed Y but only for setting Z; we do W, which neither could, because [our model / design / data]."

Building the positioning

Step 1 — The nearest-neighbor map

List the closest papers in two rings:

  • Inner ring (3–5): papers a referee will say you must beat or extend. For each: what it shows, its key assumption or limitation, and the one sentence distinguishing yours.
  • Outer ring: the broader literatures the result speaks to (this is where general interest is demonstrated). Two to three strands, each with a canonical anchor citation.

Step 2 — The "why now / why not before" clause

State why the contribution was not made earlier: a method that did not exist, data that was not available, a model that was not tractable, or a fact no one had documented. This is the spine of an original-contribution claim.

Step 3 — Confront, do not bury

Cite the closest competitor in the introduction, in the main text, not in a footnote. Referees who suspect you are hiding the nearest paper turn hostile.

Step 4 — Canonical anchors

REStud referees expect the foundational theory or methods references to be present (e.g., the seminal model your framework extends, or the identification-method papers your design relies on). Missing the obvious canonical citation reads as not knowing the field.

Checklist

  • Inner-ring papers (3–5) named, each with "what it could not do"
  • One-sentence distinguisher for each inner-ring paper
  • Outer-ring strands identified (demonstrating general interest)
  • "Why now / why not before" clause written
  • Closest competitor cited in the introduction, not a footnote
  • Canonical theory / method anchors present
  • Positioning is an argument, not a list

Anti-patterns

  • A "related literature" paragraph that is a string of "(Author, Year; Author, Year)" with no argument
  • Hiding the single closest paper to inflate apparent novelty — referees find it and it costs credibility
  • Over-claiming ("the first paper to ...") when an honest reader can name a precedent
  • Positioning only within the narrow subfield, so the general-interest claim is unsupported
  • Citing your own working papers as the main precedent while ignoring the field's anchor results

Output format

【MARGINAL CONTRIBUTION】<one sentence, relative to the literature>
【INNER RING】[paper — what it could not do — your distinguisher] x3-5
【OUTER RING STRANDS】<2-3 literatures + anchor cites>
【WHY-NOT-BEFORE CLAUSE】<one sentence>
【CANONICAL ANCHORS PRESENT】yes / missing: [...]
【NEXT SKILL】restud-identification (empirical) | restud-theory-model (theory)
信息
Category 数据科学
Name restud-literature-positioning
版本 v20260724
大小 4.33KB
更新时间 2026-07-29
语言