Identifying document relevance to Sustainable Development Goals using NLG
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
Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) techniques are considered as catalyzers of sustainable development of human society by providing information technology support for attainment of targets of Sustainable Development Goals (SDGs).The current study aims at investigating applicability of language generative models for identifying representation of SDGs in scientific publications indexed by Scopus database.The study is an initial step in developing an NLP-based framework for evaluation of attainment of SDGs based on documents written in human language.Given that SDGs are articulated in natural language in sentences of different length, comparison of their descriptions with summaries of text documents is expected to identify and quantify relevance of documents to each SDG and its targets in a more comprehensive way compared to the traditional keyword search.The study is based on abstractive summarization and follows the methodological framework presented in Figure 1.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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