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Record W2768531527 · doi:10.1145/3131609

Metatation

2017· article· en· W2768531527 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

VenueACM Transactions on Computer-Human Interaction · 2017
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of WaterlooOntario Tech University
Fundersnot available
KeywordsComputer scienceReading (process)Bridge (graph theory)Context (archaeology)AnnotationSet (abstract data type)Domain (mathematical analysis)Process (computing)Human–computer interactionInformation retrievalWorld Wide WebNatural language processingArtificial intelligenceLinguisticsProgramming language

Abstract

fetched live from OpenAlex

In the domain of literary criticism, many critics practice close reading , annotating by hand while performing a detailed analysis of a single text. Often this process employs the use of external resources to aid analysis. In this article, we present a study and subsequent tool design focused on leveraging a critic’s annotations as implicit interactions for initiating context-specific computational support that automatically searches external resources. We observed 14 poetry critics performing a close reading, revealing a set of cognitive practices supported through free-form annotation that have not previously been discussed in this context. We used guidelines derived from our study to design a tool, Metatation, which uses a pen-and-paper system with a peripheral display to utilize reader annotations as underspecified interactions to augment close reading. By turning paper-based annotations into implicit queries, Metatation provides relevant supplemental information in a just-in-time manner and acts as a bridge between close and distant reading.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0010.004
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.045
GPT teacher head0.340
Teacher spread0.295 · 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