<scp>Phenomenon‐based</scp> classification: An Annual Review of Information Science and Technology (ARIST) paper
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
Abstract While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon‐based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon‐based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon‐based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon‐based classification systems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.021 |
| Open science | 0.002 | 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