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Record W4232307868 · doi:10.7202/1084357ar

Quelle utilisation de la cartographie cognitive en matière de représentation de problèmes publics?

2017· article· fr· W4232307868 on OpenAlexaffvenue
Sofiane Laribi, Emmanuel Guy, Bruno Urli

Bibliographic record

VenueRecherches qualitatives · 2017
Typearticle
Languagefr
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsHumanitiesPublicsSociologyPhilosophyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Le présent article trouve son origine dans des travaux de recherche portant sur le rôle des représentations dans le processus d’élaboration des politiques publiques. On retrouve dans la littérature une multitude d’utilisations de la carte cognitive comme outil d’aide à la décision ou comme support à la représentation. Mais qu’en est-il de son utilisation comme outil d’aide à la représentation d’un problème public ? Cet article se veut une contribution méthodologique originale en matière d’utilisation d’outils de représentation de l’information. L’utilisation de la carte cognitive semble d’autant plus utile dans le cadre de problèmes publics où se conjuguent acteurs et intérêts divers. Les résultats obtenus attestent de la présence de représentations multiples entre les acteurs qui auraient été probablement plus difficiles à percevoir sans le recours à la carte cognitive.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.229
GPT teacher head0.465
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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