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Record W2077813743 · doi:10.3166/ria.16.339-366

Raisonnement à base de cas textuels Etat de l'art et perspectives

2002· article· fr· W2077813743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueRevue d intelligence artificielle · 2002
Typearticle
Languagefr
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhilosophyHumanities

Abstract

fetched live from OpenAlex

RESUME. Traditionnelle ment le raisonnement a base de cas (CBR) s’appuie sur des experiences decrites dans des formats completement structures tels que des objets ou des enregistrements de base de donnees. Toutefois d’autres modeles ont ete proposes pour surmonter les limitations de cette approche structurelle et rendre possible l’application a des domaines plus varies. Dans cet article, nous passons en revue les extensions du formalisme CBR proposees pour traiter des experiences decrites dans des documents textuels, travaux regroupes sous la banniere CBR textuel. Apres une presentation succincte des principes generaux du raisonnement a base de cas, nous decrivons les principaux travaux du CBR textuel et nous les comparons selon differents aspects techniques et applicatifs. Finalement, nous proposons quelques problemes et avenues de recherche meritant d’etre explores dans des travaux futurs.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.058
GPT teacher head0.316
Teacher spread0.258 · 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