Bibliographic record
Abstract
Une documentation considerable existe sur l'utilisation des groupes de consultation comme technique de recherche, et les principaux guides methodologiques concordent largement. Il est encore relativement rare, cependant, que les chercheurs en sciences de l'information utilisent cette methodologie pour explorer le comportement informationnel des enfants (par opposition aux adultes); l'experimentation ou l'observation sont les techniques de base. Cet article fait etat de l'experience des auteurs a organiser des groupes de consultation d'enfants pour obtenir leurs avis sur des portails Web existants, specialement concus pour des enfants, et sur comment ceux-ci pourraient etre ameliores. Ces groupes de consultation representaient l'etape finale d'un projet de recherche de grande envergure sur le comportement des enfants du primaire en recherche d'information dans un environnement informatique. Les groupes de consultation comportant des enfants plutot que des adultes peuvent fournir un outil methodologique estimable pour les chercheurs. Dans le cas present, cependant, non seulement les guides normatifs pour l'utilisation des groupes de consultation doivent etre soigneusement consideres, mais un certain nombre d'elements supplementaires se rapportant specifiquement aux enfants doivent etre introduis afin d'assurer la validite et la fiabilite des donnees.
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 | 0.143 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".