Mettre en récit la circulation des documents savants sur Twitter
Bibliographic record
Abstract
Les récentes études sur l’évaluation de l’impact social et l’attention envers la recherche sur les médias sociaux montrent la nécessité de changer le focus sur la signification des métriques pour s’intéresser aux contextes de circulation de la recherche. Cette étude s’inscrit dans une démarche exploratoire afin de saisir l’apport de la mise en récit pour examiner la circulation de la recherche sur Twitter à travers le cas d’un article sur les effets des changements climatiques sur les limites géographiques de l’habitat des populations de bourdons. En combinant l’analyse qualitative de tweets, l’analyse de réseaux et un entretien avec support visuel, cette étude montre comment la mise en récit de la diffusion d’un article scientifique peut jeter un éclairage spécifique à propos de sa résonance sur Twitter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.021 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".