Knowledge in the dark: scientific challenges and ways forward
Bibliographic record
Abstract
A key dimension of our current era is Big Data, the rapid rise in produced data and information; a key frustration is that we are nonetheless living in an age of ignorance, as the real knowledge and understanding of people does not seem to be substantially increasing. This development has critical consequences, for example it limits the ability to find and apply effective solutions to pressing environmental and socioeconomic challenges. Here, we propose the concept of “knowledge in the dark”—or short: dark knowledge—and outline how it can help clarify key reasons for this development: ( i) production of biased, erroneous, or fabricated data and information; ( ii) inaccessibility and ( iii) incomprehensibility of data and information; and ( iv) loss of previous knowledge. Even in the academic realm, where financial interests are less pronounced than in the private sector, several factors lead to dark knowledge, that is they inhibit a more substantial increase in knowledge and understanding. We highlight four of these factors—loss of academic freedom, research biases, lack of reproducibility, and the Scientific tower of Babel—and offer ways to tackle them, for example establishing an international court of arbitration for research and developing advanced tools for research synthesis.
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 | 0.093 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.088 |
| Scholarly communication | 0.033 | 0.077 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.011 | 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".