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Record W1503829033

Experiments with the Negotiated Boolean Queries of the TREC 2008 Legal Track

2008· article· en· W1503829033 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.

Bibliographic record

VenueText REtrieval Conference · 2008
Typearticle
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsOpen Text (Canada)
Fundersnot available
KeywordsRelevance (law)Task (project management)Computer scienceRelevance feedbackInformation retrievalRecallStandard Boolean modelRank (graph theory)Boolean expressionLearning to rankBoolean networkAnd-inverter graphTheoretical computer scienceBoolean functionData miningArtificial intelligenceAlgorithmMathematicsRanking (information retrieval)CombinatoricsPsychologyCognitive psychology
DOInot available

Abstract

fetched live from OpenAlex

Abstract : We analyze the results of several experimental runs submitted for the TREC 2008 Legal Track. In the Ad Hoc task, we found that rank-based merging of vector results with the reference Boolean results produced a statistically significant increase in mean F1@K and Recall@B compared to just using the reference Boolean results. In the Relevance Feedback task, we found that the investigated relevance feedback technique, when merged with the reference Boolean results, produced some substantial increases in Recall@Br without any substantial decreases on individual topics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.250
Teacher spread0.204 · 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