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Record W1542335942 · doi:10.20360/g2759s

‘Tech FTW!!!’ Ninth Graders, Romeo and Juliet, and Digital Technologies

2013· article· en· W1542335942 on OpenAlexaffvenue
Roberta Hammett

Bibliographic record

VenueLanguage and Literacy · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsWestern University
Fundersnot available
KeywordsNinthAcronymAction researchFace (sociological concept)LiteracyAction (physics)Mathematics educationSociologyComputer sciencePedagogyPsychologyLinguisticsSocial science

Abstract

fetched live from OpenAlex

This article describes an action research study in which ninth graders composed digital responses to their study of Shakespeare’s Romeo and Juliet. Their collaborative digital projects included e-zines, presentations, digital videos and photostories. Although meeting face to face in and outside of class, the students also used Ning (a social networking site) to share their products and to communicate about them throughout the unit. The paper will focus on what their communications reveal about the students’ participation and understanding during the composing process, including thinking, representing, actively engaged, creating, knowing, and social - summed up in the acronym TRACKS.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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