Bibliographic record
Abstract
The environmental sciences are characterized by their boundless interdisciplinarity and cannot be discussed independently from other scientific fields such as ecology, engineering, and climatology. The complex nature of the discipline leads to challenges in placing it within a controlled vocabulary such as the Library of Congress Subject Headings (LCSH). However, the placement of a term within a thesaurus hierarchy has potential repercussions for the discoverability of materials assigned that subject heading. As the environmental sciences are rapidly expanding due to global climate change, accurate representation of this discipline within a widely used vocabulary is crucial. In this paper we employ a visual mind mapping technique to examine how the environmental sciences are represented by codified language within the LCSH, then complete a domain analysis of the field to determine how environmental science represents itself. In comparing these two analyses we determine that the LCSH subject headings do not capture the interdisciplinary nature of the field in two primary ways; the term Environmental sciences is not sufficiently connected to the terms representing other major scientific subjects essential for a foundational understanding of environmental science, and key forward-looking topics of concern to environmental scientists such as Climatology are not in direct relationships with Environmental sciences. Correcting these issues is an important task, as ensuring that researchers are able to access a full range of environmental science materials will aid in finding sustainable climate solutions for our planet.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.019 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".