Bibliographic record
Abstract
How do scientists maintain their research programs in the face of not finding anything? Continual failure to produce results can result in declining support, scientific controversy and credibility challenges. We elaborate on a crucial mechanism for sustaining the credibility of research programs through periods of non-detection: the maintenance of ambiguity. By this, we refer to scientific strategies that resist closure or an experiment's premature end by creating doubt in negative findings and fostering hope for future positive results. To illustrate this concept, we draw upon the recent history of Martian exobiology. Since the 1960s, planetary scientists have continually tried and failed to find evidence of life on Mars. And yet, interest in extraterrestrial life detection remains high, with more missions to Mars underway. Through three destabilizing events of non-detection, we show how exobiologists sustained the search for Martian life by casting doubt on negative findings, pointing to other possible unexplored routes to success, and finally reconfiguring operations around new methods or goals. New approaches may take the form of shifts in scale, method and object of interest. By pivoting to a different scale, method or object, exobiologists have continued to study a subject continually lacking proof of existence and made important discoveries about life on Earth.
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 | 0.080 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.082 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".