Scientific Publications: Moving beyond Quality and Quantity toward Influence
Bibliographic record
Abstract
A debate continues on the relative importance of quality and quantity in scientific publication. Recent published correspondence (Fischer et al. 2012) heralds a movement to reemphasize quality research over quantity. Although we certainly agree with the call for quality, decrying quantity likewise poses a trade-off that may ultimately be undesirable for fostering an impactful body of research and advancing science. Instead, we argue for an integrated view of scientific contributions that incorporates elements of both quality and quantity. We describe this view as influence. Quality refers to the standard of something as measured against something similar. In a research context, this is inherently problematic, because it is challenging to make such subjective comparisons. For example, is a single paper published in a “top-tier” high-impact journal, which is consequently likely to be broadly read and cited, a more valuable contribution to a research field than two or more papers published in “lower-tier” journals (Loyola et al. 2012)? Quantity is more straightforward to define, because it refers to the number of publications generated by an individual researcher or a research group. However, simply counting the number of publications fails to provide an indication of the quality of the work. Quality is nebulous, whereas quantity is more tractable, but neither attribute alone provides an adequate assessment of the full value of a scientific contribution.
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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 | 0.110 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.055 | 0.084 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".