MétaCan
Menu
Back to cohort
Record W1880500695 · doi:10.1002/spe.2122

Fast and effective soft links

2012· article· en· W1880500695 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

VenueSoftware Practice and Experience · 2012
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceHypertextMarkup languageSource codeJavaCode (set theory)TraceabilityInformation retrievalSignature (topology)Programming languageDatabaseWorld Wide WebXMLSoftware engineering

Abstract

fetched live from OpenAlex

SUMMARY Inspired by requirements traceability problems, we present a method for implementing fast and effective hypertext links to specific locations within documents. These soft links do not depend on tags, markup, or closed tool sets, yet they can generally survive extensive edits to a document collection, allowing the targets of these links to be located in real collections after years of ongoing and frequent changes. We base our implementation of soft links on an existing passage retrieval algorithm, originally designed for question answering. The method treats the text surrounding the target of a soft link as a passage to be retrieved, creates a signature for that passage, and resolves the link by searching for the passage. The method is evaluated over a large collection of text and two large collections of source code, one written in the C programming language and one written in Java. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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