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Record W3031669116 · doi:10.29085/9781856049740.005

Task-based information searching and retrieval

2018· book-chapter· en· W3031669116 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

VenueFacet eBooks · 2018
Typebook-chapter
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTask (project management)Information retrievalComputer scienceSet (abstract data type)Cognitive models of information retrievalService (business)Word (group theory)Information needsInformation systemHuman–computer information retrievalWorld Wide WebSearch engineEngineeringMathematics

Abstract

fetched live from OpenAlex

Information retrieval systems are purposeful devices developed to service multiple objectives from locating the current weather conditions to identifying critical evidence for use in complex decision making. Those systems evolved from extracting bibliographic data held in large libraries of references, to retrieving nuggets of information from full-text repositories. However, we tend to think of information retrieval systems simply as generic search systems that respond to a query with a set of results to meet some information need, rather than purposeful applications whose raison d'être is to deliver task-specific information that leads to problem resolution. The early discussions about information retrieval systems implicitly and erroneously equated the concept of task with a user's information need, problem, question or request (e.g. Saracevic et al., 1988; Saracevic and Kantor, 1988a, 1988b; Tague-Sutcliffe, 1992); the exact word used depended on the decade and origin of the work. The task that triggered the need was usually considered peripheral to the research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.856
Threshold uncertainty score0.720

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 science0.0000.000
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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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