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Record W2492872696 · doi:10.1017/cbo9780511817489.013

Appendix: <i>Australian Standards for Editing Practice</i>

2004· other· en· W2492872696 on OpenAlexaboutno aff
Janet Mackenzie

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsAppendixComputer scienceLibrary scienceBiology

Abstract

fetched live from OpenAlex

Preface Editors are central to any publishing project; they endeavour to reconcile the needs of the author, the reader and the publishing client. Editors look at the publication as a whole as well as at the detail. They ensure that the focus, structure, language, style and format of a publication suit its purpose and readership, and prepare the final copy to a standard of quality suitable for the publication. Australian Standards for Editing Practice covers the knowledge and skills expected of experienced editors, although editors' workplace responsibilities and the requirements of particular projects will determine the relevance of each standard. Editors also recognise when they need to find out and apply specialised knowledge from other sources or professions. These standards have been developed for editors to use: as a basis for judging the comprehensiveness of their own knowledge and skills when promoting themselves and the editing profession generally. They will also help publishing clients understand the range of services editors provide, and guide educational institutions in developing editing courses. These standards were devised by the Standards Working Group of the Council of Australian Societies of Editors (CASE), approved by the members of all Australian societies of editors, and ratified by CASE. They are to be reviewed at least every three years; please address comments to the closest member society. The working group used the Editors' Association of Canada's Professional Editing Standards as a starting point and the Canberra Society of Editors' Commissioning Checklist as a reference.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.315
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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
GenreOther

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
Published2004
Admission routes1
Has abstractyes

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