How to prepare the video component of the Diachronic Corpus of Political Speeches for multimodal analysis
Bibliographic record
Abstract
The Diachronic Corpus of Political Speeches (DCPS) is a collection of 1,500 full-length political speeches in English. It includes speeches delivered in countries where English is an official language (the US, Britain, Canada, Ireland) by English-speaking politicians in various settings from 1800 up to the present time. Enriched with semi-automatic morphosyntactic annotations and with discourse-pragmatic manual annotations, the DCPS is designed to achieve maximum representativeness and balance for political English speeches from major national English varieties in time, preserve detailed metadata, and enable corpus-based studies of syntactic, semantic and discourse-pragmatic variation and change on political corpora. For speeches given from 1950 onwards, video-recordings of the original delivery are often retrievable online. This opens up avenues of research in multimodal linguistics, in which studies on the integration of speech and gesture in the construction of meaning can include analyses of recurrent gestures and of multimodal constructions. This article discusses the issues at stake in preparing the video-recorded component of the DCPS for linguistic multimodal analysis, namely the exploitability of recordings, the segmentation and alignment of transcriptions, the annotation of gesture forms and functions in the software ELAN and the quantity of available gesture data.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.050 |
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".