Cognition, Ecology, and Tok Pisin Folktales
Bibliographic record
Abstract
From its early development catalyzed by mass displacement and forced relocation of Melanesian people, Tok Pisin has flourished into a thriving language of mixed origins. English acts as its main superstratum with elements from German and Patpatar-Tolai being significant contributors to its lexicon. For the last half a century, this diversity and complex genealogy have made Tok Pisin a subject of interest for linguistics, leading to multiple dictionaries, detailed grammar descriptions, and most importantly for this study, the documentation of traditional folktales. This study proposes that Tok Pisin folktales are not just carriers of traditional knowledge but cognitive-ecological cultural models (Shore 1996). This means they encapsulate a worldview in which mind, language, and environment are interwoven. Using concepts from cognitive linguistics and ecolinguistics, this paper sets out to analyze how Tok Pisin narratives model ecological understanding through their language. It will use a corpus of over 1000 traditional folktales which were originally recorded in the Wantok Newspaper from 1972-1997 (Slone 2001). There is a growing body of literature on the topic of cognitive-ecolinguistics for Tok Pisin, with significant developments coming from Rajdeep Singh’s 2022 paper on cognitive schemata in Tok Pisin. Singh’s paper indicates that the cognitive conceptualization of Tok Pisin in the ecological sense is that of the Oceanic languages, instead of the main substratum language(Singh 2022: 4-5). Additionally, Krzysztof Kosecki builds upon this research in his upcoming paper and further solidifies the argument that nature related vocabulary reflects the Oceanic cognitive model which indicates that humans are conceptually integrated with nature, but not only this, that nature terms are also used to understand a multitude of other concepts that might seem unrelated from a Western perspective (Kosecki 2025: 52). This paper expands on these two pieces of recent scholarship by incorporating the corpus of traditional folktales. Both Singh and Kosecki have primarily focused on isolated lexical items and constructions rather than on extended discourse and narratives. This then leaves the idea of how ecological cognition operates within the narrative context and how the folktales linguistically model the cognitive environmental relationship
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".