Why did the Canada goose cross the sea? : accounting for the behaviour of wildlife in the documentary series Life
Bibliographic record
Abstract
The language investigated here comprises commentaries to a television documentary series about wildlife. We explore debates about the implications of evolutionary theory for accounts of animals' behaviour, and the challenge facing broadcasters seeking to explain this to a general audience. Our analysis, which was supported by concordancing software, focuses specifically on deontic and dynamic modal constructions. We identify four kinds of ‘obligation’ to which the non-human creatures featured in these texts are represented as being subject. We suggest that the modal system of English is implicated in the inevitable tendency in these broadcasts towards both anthropomorphic and teleological explanations of animals' behaviour. We conclude that applied linguists have a contribution to offer as broadcasters make decisions about such linguistic choices. In diesem Beitrag werden die Kommentare einer Naturdokumentationsreihe sprachlich analysiert. Wir untersuchen, inwieweit evolutionäre Theorien Tierverhalten erklären können, und wie die Produzenten mit der Herausforderung umgehen, diese Sachverhalte einem Laienpublikum nahezubringen. Unsere Analyse, gestützt auf Konkordanzen, konzentriert sich insbesondere auf deontische und dynamische Modalkonstruktionen. Wir unterscheiden vier Arten der ‘Obligation’, in denen die Tiere in der Dokumentation als Subjekt repräsentiert werden. Wir zeigen, daß das Modalsystem des Englischen unausweichlich dazu führt, anthropomorphische und teleologische Erklärungen des Tierverhaltens zu verwenden. Wir kommen zu dem Schluß, daß die angewandte Sprachwissenschaft die Produzenten von Naturdokumentationen bei derartiger Sprachwahl unterstützen kann.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".