Pragmasemantic Categorization of Terms for Representing Climate Knowledge 


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Creation and Description of the trees
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The trees represent the most frequently used terms (see below for the thresholds defined for term selection) by different communities in their discourse on climate change. Because these terms index specialised knowledge, they provide insight into the types of knowledge that define and structure the field of anthropogenic climate change (ACC). Given the non-prototypical nature of this domain, marked notably by strong transdisciplinarity, we adopt both an inductive and deductive approach to categorising terminological units. This approach allows us to propose a categorisation tailored to the ACC field, rather than starting from pre-established semantic categories (such as those, for example, proposed by the UCREL Semantic Analysis System (USAS) (Rayson et al. 2004)). We refer our readers to our thesis (Chap. 5, pages 138-141) for a description of the methodology used for term categorisation.

The different tree diagrams in this folder represent the categories resulting from this categorisation process, conducted on the terms from terminological layers 1 and 2 (Bureau 2023, Chap. 5). We include different denominative variants of the same concept, provided that the variants in question exceed the same frequency threshold. In this sense, these trees consist of terms rather than concepts, while allowing, through the identified categories, a conceptual perspective on the domain. This approach is motivated by the fact that representing only concepts would have required choosing the designation to materialise each of the concepts in question, a choice that would necessarily have had a subjective or arbitrary dimension.


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Specificities of the "Experts" tree
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The terms represented in this tree all have a frequency of at least 30 in the corpus, which combines the UN and NGO corpora. This tree aims to represent the terminology used by "climate expertise" in a broad sense.

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Specificities of the "NGOs vs. IGOs" tree
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The terms represented in the blue bubbles in this tree all have a positive Inverse Document Frequency (IDF) (expressed as a percentage) and a specificity score higher than 3.09 in the NGO corpus, while those classified in the green bubbles or without coloured backgrounds meet these criteria respectively in the IGO corpus only or in both corpora. The list of these terms corresponds to the file "TGCC NGO/IGO" (folder "Expert Terminology") in this repository, where the columns "scoreIDFxspé_IGO" and "scoreIDFxspé_NGO" correspond to the product of the IDF and specificity scores (expressed as percentages) in the IGO and NGO corpora, respectively. This tree thus aims to compare the terminology used by IGOs and NGOs, and to account for the terms they share.

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Specificities of the "Press" trees
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The terms represented in the "Press_COP15", "Press_COP21", and "Press_COPs25-26" trees all have a relative frequency of at least 15 in the "Press_COP15", "Press_COP21", and "Press_COPs25/26" corpora, respectively. These trees aim to represent the dissemination of expert knowledge to the press between COPs 15, 21, and 25/26, as well as the terms specific to the press (classified in the orange bubbles).


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References
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Bowker, Lynne. 1993. ‘Multidimensional Classification of Concepts for Terminological Purposes’. 4th ASIS SIG/CR Classification Research Workshop, 39–56. DOI: https://doi.org/10.7152/acro.v4i1.12610.


Bowker, Lynne. 2022. ‘Chapter 6. Multidimensionality’. In Faber, Pamela & Marie-Claude L’Homme (eds). Theoretical Perspectives on Terminology: Explaining Terms, Concepts and Specialized Knowledge. Terminology and Lexicography Research and Practice. Amsterdam: John Benjamins Publishing Company, 353–76. DOI: https://doi.org/10.1075/tlrp.23.06bow.


Bureau, Pauline. 2023. " Variation terminologique et néologie dans le domaine du changement climatique ". PhD thesis in Applied linguistics, Grenoble Alpes University.

Faber, Pamela. 2022. ‘Chapter 16. Frame-Based Terminology’. In Faber, Pamela & Marie-Claude L’Homme (eds). Theoretical Perspectives on Terminology: Explaining Terms, Concepts and Specialized Knowledge. Terminology and Lexicography Research and Practice. Amsterdam: John Benjamins Publishing Company, 353–76. DOI: https://doi.org/10.1075/tlrp.23.16fab.


Rayson, Paul, Dawn Archer, Scott Piao, and Tony Mcenery. 2004. ‘The UCREL Semantic Analysis System’. Proceedings of LREC-04 Workshop: Beyond Named Entity Recognition Semantic Labeling for NLP Tasks, 7–12. Lisbon, Portugal.