La littératie administrative, un concept à exploiter pour améliorer les services publics
Bibliographic record
Abstract
Administrative literacy represents the skillset needed to successfully carry out an administrative process (Döring, 2021), such as the driving license exchange. In Quebec, newly arrived immigrant citizens are required by the Société de l’assurance automobile du Québec (SAAQ) to exchange their original driving license for a Quebec one, among other procedures. Newly arrived immigrant are vulnerable citizens (Roy, 2010), and they were left out when the Quebec government embarked on a major project to simplify communications after passing the Public Administration Act. However, to ensure a better understanding of the message it seeks to convey, the SAAQ should consider the context in which the communication takes place as well as the users to whom the message is addressed. After interviewing workers in newcomers’ centers, enabling us to create an experience map, we found that they’re often playing the part of literacy mediators (Papen, 2010) whose role is to bridge the gap between the immigrants’ literacy levels and those required by the Quebec administration. However, their roles’ parameters remain to be defined. Their training seems incomplete and the SAAQ responsibility is not sufficiently marked. By pursuing our master’s project further, we shed light on the multiple possibilities carried out by such a research project, thus, emphasizing the social relevance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".