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Record W2548558551 · doi:10.1017/s0261444816000100

Developing illustrative descriptors of aspects of mediation for the Common European Framework of Reference (CEFR)

2016· article· en· W2548558551 on OpenAlexaff
Brian North, Enrica Piccardo

Bibliographic record

VenueLanguage Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMediationVariety (cybernetics)Process (computing)EpistemologyObject (grammar)Reflection (computer programming)Meaning (existential)Focus (optics)Space (punctuation)PsychologyComputer scienceSociologyCognitive scienceLinguisticsArtificial intelligenceSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

The notion of mediation has been the object of growing interest in second language education in recent years. The increasing awareness of the complex nature of the process of learning – and teaching – stretches our collective reflection towards less explored areas. In mediation, the immediate focus is on the role of language in processes like creating the space and conditions for communication and/or learning, constructing and co-constructing new meaning, and/or passing on information, whilst simplifying, elaborating, illustrating or otherwise adapting input in order to facilitate the process concerned. At a deeper level though, the notion of mediation embraces a broader spectrum of dimensions and connotations. Mediation has been defined as a ‘nomadic notion’ (Lenoir 1996) insomuch as it is at the core of a variety of scientific disciplines and the term ‘mediation’ is used in different senses in different contexts.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0040.010
Scholarly communication0.0070.011
Open science0.0020.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.060
GPT teacher head0.299
Teacher spread0.238 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations112
Published2016
Admission routes1
Has abstractyes

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