Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Novelty bias is the tendency for an intervention to appear better when it is new. It is also known as the ‘novel agent effects’ or ‘fading of reported effectiveness’.1 2 The mechanisms by which interventions appear better when new or new for a specific purpose are unknown and may involve other forms of bias having a more significant effect when an intervention is new. Novelty bias can arise when the internal or external validity is compromised. Regarding internal validity, performance bias3 and detection bias4 may cause novelty bias because unblinded researchers may be particularly enthusiastic about new treatments, leading to differences in the care received by the intervention and control groups apart from the intended treatment or differences in the outcome assessment. Selective outcome reporting bias can also be a critical reason for novelty bias.5 6 Positive result bias7 (eg, positive results of a treatment are selectively reported when it is new and less selectively reported later), confirmation bias8 (eg, only the evidence supporting the new treatments is gathered while the others are disregarded) and hot stuff bias9 (eg, researchers may be keen to confirm the positive findings regarding a new and hot topic rather than falsifying them) are examples of selective reporting bias. They can lead to overinterpretation of the point estimates …\n
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it