Title: Merit or Networks? Determinants of Research Publication

Original: arXiv:2606.03763v1 Announce Type: cross Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it has resisted decades of study because a paper's quality could not be gauged ahead of its publication fate without using that fate as the yardstick. We break this constraint by measuring a paper's idea quality directly from its text, before publication, using a discipline-trained LLM evaluator that scores the idea without seeing author names or outcomes. Using economics as a case study, we combine this text-legible idea-quality score with an execution-quality rubric, a connection index, an author-ability index, and an off-the-shelf language-model text score to estimate a five-input production function for journal placement across 6,208 economics working papers. The inputs are not rivals but a sequence along the ladder of prestige. Execution sets a meritocratic floor and is the largest input overall. Text-legible idea quality grades the rungs in between. Connections set a favoritism ceiling that bites mainly near the apex, the most selective journals. Connections work through two additive channels: connected authors write papers that score higher, and at equal scores their papers are still more likely to place better. Yet this advantage is bounded. Connections raise the odds of every rung without making the apex the typical outcome for ordinary ideas, and even the highest-scoring papers face real friction reaching the visible journal ladder. The result nests, rather than chooses between, the meritocracy and network accounts of how science is published.

Rewrite: Does academic publishing prioritize the strength of intellectual concepts or the leverage of professional relationships? This dilemma permeates the prestige-oriented landscape of modern science, yet it has long evaded resolution. Researchers have struggled to isolate idea quality from publication success, as assessing the former typically required using the latter as a benchmark. Our study circumvents this circular logic by evaluating concept quality directly from manuscript text prior to publication. We employ a domain-specialized large language model (LLM) to assign quality scores, ensuring the evaluator remains blind to author identities and eventual publication results.

Focusing on the field of economics, we analyze 6,208 working papers to construct a five-component production function that predicts journal placement. This model integrates a text-derived idea-quality metric, a rubric for execution quality, a connection index, an author-ability index, and a standard language-model text score. Rather than competing as mutually exclusive factors, these inputs operate sequentially along a hierarchy of prestige.

Execution quality establishes a meritocratic baseline and serves as the most significant overall contributor. Idea quality, derived directly from the text, determines positioning on the intermediate rungs of this hierarchy. Meanwhile, professional connections establish a ceiling based on favoritism, exerting its primary influence at the apex—specifically, within the most exclusive journals.

Connections enhance publication prospects through two distinct mechanisms: first, individuals with strong networks tend to produce papers that inherently receive higher scores; second, even when controlling for identical scores, connected authors are more likely to secure better placements. However, this network advantage is limited. While connections improve the probability of ascending each tier, they do not guarantee that average ideas will reach the top tier. Furthermore, even the highest-quality manuscripts encounter significant hurdles in reaching prominent journals. Ultimately, our findings suggest that both meritocratic principles and network effects coexist in shaping scientific publishing outcomes, rather than one excluding the other.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC