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Record W4414503355 · doi:10.37213/cjal.2025.34475

Collaboration in the revision of a piece of writing

2025· article· en· W4414503355 on OpenAlexvenueno aff
Carmen Rodríguez Gonzalo

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Process (computing)Natural (archaeology)Corrective feedbackNatural languagePhase (matter)Composition (language)Test (biology)Language acquisition

Abstract

fetched live from OpenAlex

The importance of revision has been recognised by numerous scholars of the teaching and learning of writing (Abad & Rodríguez-Gonzalo, 2023; Bereiter & Scardamalia, 1987; Camps, 2020; Horning & Becker, 2006), especially if it is understood as a recursive and transversal phase that affects all levels of language (Álvarez Angulo, 2011; Camps, 2020; Marin & Legros, 2006). To test whether group revision is an effective tool in the teaching-learning process of writing in Spanish as a first language, in this paper we analyse large group revision sessions recorded in the natural context of a university classroom in which we implemented a didactic writing sequence (Camps, 2020; Dolz et al., 2001; Rodríguez-Gonzalo & Abad-Beltrán, 2023). The objective of this paper—an instrumental case study (Stake, 2007; Yin, 2009)—is to describe the interactions that take place in this revision process, categorise the aspects of the different linguistic levels to be identified and describe the students' proposed actions aimed at modifying the texts. The results show that approaching the revision phase collectively encourages the development of the students' metalinguistic and meta-rhetorical awareness (Horning, 2006), provided that this phase is not limited to the correction of errors in the final text and is conceived as a complex process that represents growth, progress and discovery (Haar, 2006) in the composition of a piece of writing.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.343
Teacher spread0.327 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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