Night Matters—Why the Interdisciplinary Field of “Night Studies” Is Needed
Bibliographic record
Abstract
The night has historically been neglected in both disciplinary and interdisciplinary research. To some extent, this is not surprising, given the diurnal bias of human researchers and the difficulty of performing work at night. The night is, however, a critical element of biological, chemical, physical, and social systems on Earth. Moreover, research into social issues such as inequality, demographic changes, and the transition to a sustainable economy will be compromised if the night is not considered. Recent years, however, have seen a surge in research into the night. We argue that “night studies” is on the cusp of coming into its own as an interdisciplinary field, and that when it does, the field will consider questions that disciplinary researchers have not yet thought to ask.
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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 | 0.064 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.016 | 0.040 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".