The <i>Matthew </i>effect and early-career setbacks in research funding:a replication study (RoRI Working Paper No. 16)
Bibliographic record
Abstract
Research funding plays an important role in academic careers. Previous research showed that researchers who obtain early-career funding are more likely to obtain later-career funding, whereas researchers who do not obtain early-career funding show a higher citation impact when reapplying for later-career funding. We replicate the so-called Matthew effect and early-career setback effect across fourteen different funding programmes from six research funders across Europe and North America. Earlier studies rely on studying applicants close to the funding line, and the inferred effects are limited to this “grey zone”. We study the robustness and generalisability of both effects to the whole population, beyond applicants in the “grey zone”. We find that the Matthew effect replicates, is robust across funders and model specifications, and generalises to the whole population. The early-career setback effect also replicates, but is not robust across funders and model specifications, and does not generalise to the whole population. We suggest that the early-career setback observation is due to a selection effect of unfunded applicants being particularly more likely to reapply later if they have a high citation impact. To address the Matthew effect, research funders and research organisations could consider stimulating promising rejected applicants to reapply. Another possibility would be to diminish potential deleterious effects of funding on academic careers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".