Concealing research outcomes: Are there times when it is actually justified?
Bibliographic record
Abstract
Sir, The recently published informative and well-written editorial from Dr. Bhaskar[1] addressing the reasons why as much as 60% of research results go unpublished provides several plausible and well-documented reasons including overt publication bias[2] and the “file drawer effect.” Although I agree that these effects could, in part, be related to researchers' inherent biases not to report the results of negative studies (thinking that journals' past tendencies that favour publishing positive over negative study results will reduce their chances of manuscript acceptance), other factors might also be at play. Indeed, although this type of publication bias may have been common in the past,[3] and is hopefully on the decline, there is also a large amount of research that is undertaken that involves poor study design, including studies that are either too small (and underpowered) or include an overly optimistic effect size in their power calculation. Thus, many “negative” studies are only negative (and thus “filed away” by investigators) because they were underpowered from the start and proceeding with publishing an a priori – designed underpowered trial can contribute to some distortion of the literature. So, whereas I am a firm believer that all well-conducted research should find a public forum for dissemination (i.e., through print publication), the risk of publishing negative underpowered studies cannot be overlooked. Although 60% of research is arguably far too large an amount of unpublished data, some research should arguably never be published for the single reason that it likely should not have been undertaken in the first place. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 | 0.235 | 0.720 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.036 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".