The InsightsNet Climate Change Corpus (ICCC)
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 The discourse on climate change has become a centerpiece of public debate, thereby creating a pressing need to analyze the multitude of messages created by the participants in this communication process. In addition to text, information on this topic is conveyed multimodally, through images, videos, tables and other data objects that are embedded within documents and accompany the text. This paper presents the process of building a multimodal pilot corpus to the InsightsNet Climate Change Corpus (ICCC) and using natural language processing (NLP) tools to enrich corpus (meta)data, thus creating a dataset that lends itself to the exploration of the interplay between the various modalities that constitute the discourse on climate change. We demonstrate how the pilot corpus can be queried for relevant information in two types of databases, and how the proposed data model promotes a more comprehensive sentiment analysis approach.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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