Résultats d’un pré-test visant à évaluer l’efficacité d’une communication administrative gouvernementale (note de recherche)
Bibliographic record
Abstract
This research note presents partial results of a user test assessing the “communicative effectiveness” (Clerc & Beaudet, 2008) of a written communication aimed at citizens penalized for driving under the influence. The doctoral candidate is interested in the users interaction with an informational ecosystem composed of multiple sources of information. She compares the effectiveness of three ecosystems by checking their effects on users' comprehension, appreciation, and mental workload. These objectives require expanding the usual scope of the “communicative effectiveness” concept and exploring aspects related to reading/using multiple texts in an administrative context. The tests were conducted with 15 French-speaking adults without a high school diploma. An adapted version of the NASA-TLX questionnaire (Hart & Staveland, 1988) was used to measure mental workload. The data suggest that when faced with a letter of a high informational density, users with low literacy levels focus their attention on peripheral details at the expense of the main ideas. No correlation was observed between comprehension and mental workload. The data are now considered a "pre-test" used to refine the research design for a second wave of interviews.
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 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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".