0-shot text classification for web-based environmental indicators: Pilot study on B-Corp data
Bibliographic record
Abstract
This paper proposes a tool that uses web-based information to generate a proxy for the environmental culture indicator developed by B-Lab. The tool is based on recent advances in Natural Language Processing (NLP), such as pre-trained language models like BART that better capture the semantic facets of natural language. The algorithm and data provide several advantages, including real-time analysis, minimal building cost, granularity, and a large sample size, making it appealing. The Zero-shot text classification task is used to create an indicator of companies' environmental culture, which was chosen due to the urgency created by recent climatic events, pushing for increased environmental protection and sustainability culture promotion. The tool was tested on the B-CORP dataset, which provides scores on environmental performance. Results indicate that scores for certain environmental topics generated by the tool are correlated with B-Lab's environmental indicator. This research open door to the possibility of predicting the environmental readiness of the companies base on web-based indicators.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".