Modes of Annotation in the Video-Based Corpus FrancoToile: Developing a Design Method
Bibliographic record
Abstract
In corpus linguistics, texts are typically annotated in order to focus the attention on: (a) the form of the text or words, and (b) the structure of sentences (that is, morphological and syntactic tagging). Yet, when dealing with language learning and the development of skills other than just linguistic ones, other types of annotations are needed. Annotating with either a specific learner or pedagogy in mind often engages the researcher in more complex issues than the ones just related to corpus linguistics. In this article, we report on the methods used to create a digital library of videos and annotated transcripts called FrancoToile (http://francotoile.uvic.ca). As a needs-driven corpus, FrancoToile includes annotations within the video transcripts in order to help users develop their cultural and linguistic literacies in French. These annotations must relate directly to the purpose of the system (the development of cultural and linguistic literacies) and to the specific skill or competency that we hope language learners will gain. We analyze learning needs, modify the software, and observe and engage with users on an ongoing basis to create a language tool that will better address users’ needs. This approach of incorporating user feedback increases the usefulness of the annotated videos. We continue to seek means to encourage the involvement of users, both teachers and learners, in the process of corpora editing and content building.
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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 | 0.043 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".