Instructions for the Storytelling in Sequence Task (STST) by Fossard, Achim et al.
Bibliographic record
Abstract
The Storytelling in Sequence Task (STST) is a joint task allowing the production of narrative discourse. The STST was introduced in a paper by Fossard, Achim et al (2018) - see full reference below, and is also available in French under the name "Tâche de narration d'histoires en séquences (TNHS)". Here, we share the version of the task instructions that were translated and adapted in English by the team of Amélie Achim (Université Laval) in the context of a research project (the Mots+ project, by Lena Palanniyappan, Amélie Achim et al.). The stimuli for the task, with the words in the images translated in English, can be accessed at: https://doi.org/10.5281/zenodo.12633286. The original stimuli and instructions in French can be respectively accessed at: https://doi.org/10.5281/zenodo.11544087 and https://doi.org/10.5281/zenodo.12697758. Reference for the task: Fossard M, Achim AM (co-first authors), Roussier-Vercruyssen L, Gonzales S, Bureau A, Champagne-Lavau M. (2018). Referential choices in a collaborative storytelling task: Discourse stages and referential complexity matter. Frontiers in Psychology: Language Sciences, vol. 9, article 176. doi: 10.3389/fpsyg.2018.00176
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.244 | 0.146 |
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".