MétaCan
Menu
Back to cohort
Record W1897315496 · doi:10.19173/irrodl.v10i3.579

Sage without a Stage: Expanding the Object of Teaching in a Web-Based, High-School Classroom

2009· article· en· W1897315496 on OpenAlexafffundvenueabout
Elizabeth Murphy, María A. Rodríguez‐Manzanares

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)MetaphorDistance educationMathematics educationPedagogyIndependence (probability theory)Object (grammar)SociologyPsychologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

This paper reports on a study that uses cultural historical activity theory (CHAT) to make sense of e-teachers’ activity in a context of high-school distance education. Data collection involved semi-structured interviews with 13 e-teachers as well as seven management and support personnel in an organization responsible for the design and delivery of high-school distance education in the province of Newfoundland and Labrador, Canada. As well, the authors conducted a second round of interviews with 12 of the 13 teachers. Findings revealed that the traditional metaphor of teacher as ‘sage on the stage’ ceased to have a reference point in the distributed online classroom. The e-teachers were widening the object of their activity to include less teacher-centered forms of learning that involved more student independence.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.115
GPT teacher head0.520
Teacher spread0.405 · 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

Citations28
Published2009
Admission routes4
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicInnovative Education and Learning PracticesFrench-language works237,207