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Record W2979220330 · doi:10.18806/tesl.v36i1.1309

Revisiting Proofreading in Higher Education: Toward an Institutional Response to Editors Canada’s Guidelines for Ethical Editing of Student Texts

2019· article· en· W2979220330 on OpenAlexvenueaboutno aff
Nina Conrad

Bibliographic record

VenueTESL Canada Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsProofreadingAcademic writingTechnical writingHigher educationSociologyPedagogyPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Anglosphere universities are a site of growing concern about students’ use of professional English language editing and proofreading services for the correction of academic writing. Students’ use of such services raises issues of ethics and academic integrity as well as fundamental questions about how value is allotted to the labour involved in producing written texts and providing writing instruction. In addition, the term proofreading is ambiguous, obscuring the extent to which proofreaders intervene in students’ written texts. Although much attention has been focused on graduate students’ use of proofreading services, there is growing recognition that some undergraduates receive proofreading as well. In response to these issues, Editors Canada (2018) has recently released new guidelines for ethical editing of student texts that, for the first time, delineate standards for professional editing of both undergraduate and graduate students’ writing. These guidelines are effective in clarifying acceptable practices for editors, students, and university instructors, yet they apply only to students who seek proofreading from an Editors Canada affiliate. This essay summarizes key considerations in the proofreading debate with the aim of encouraging the development of clearer institutional and classroom policies on proofreading that will complement the Editors Canada guidelines and apply to all students.
 Les universités de l’anglosphère s’inquiètent de plus en plus de l’utilisation que font les étudiants et étudiantes de services professionnels de révision et de correction pour leurs textes académiques de langue anglaise. Le recours à de tels services de la part des étudiants soulève des questions d’éthique et d’intégrité académique en plus de questionnements fondamentaux sur l’attribution de la valeur accordée au travail exigé pour la production de textes écrits et l’enseignement de l’écriture. Qui plus est, le terme de correction est ambigu puisqu’il ne permet pas de déterminer dans quelle mesure les correcteurs interviennent dans les textes écrits des étudiants. Si on accorde une attention grandissante au recours des étudiants de cycle supérieur aux services de correction, on reconnaît de plus en plus que certains étudiants de premier cycle y font appel eux aussi. En réponse à ces questions, Réviseurs Canada (2018) a récemment publié de nouvelles directives pour la révision éthique de textes rédigés par des étudiants qui délimitent pour la première fois les normes applicables à la révision professionnelle de textes d’étudiants de premier cycle et de cycle supérieur. Ces normes définissent clairement les pratiques acceptables chez les réviseurs, les étudiants, et les enseignants universitaires, mais elles s’appliquent uniquement aux étudiants qui recourent à des services de correction de texte affiliés à Réviseurs Canada. Le présent essai résume les principales considérations du débat sur la correction de texte dans le but de permettre d’offrir aux établissements et aux salles de classe des politiques de correction plus claires susceptibles de compléter les directives de Réviseurs Canada et de s’appliquer à l’ensemble de la population étudiante.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.402
Teacher spread0.316 · 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.

Study designNot applicable
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

Citations7
Published2019
Admission routes2
Has abstractyes

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