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Academic publishing: Making the implict explicit

2016· article· en· W2779772067 on OpenAlexaffabout
Cecile Badenhorst, Xiaolin Xu

Bibliographic record

VenuePreprints.org · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPublishingNarrativeNegotiationContext (archaeology)SociologyPublicationAcademic writingMedia studiesSocial sciencePedagogyPolitical scienceLiteratureHistoryLawArt

Abstract

fetched live from OpenAlex

For doctoral students, publishing in peer reviewed journals is a task many face with anxiety and trepidation. The world of publishing, from choosing a journal, negotiating editors and navigating reviewers’ responses is a bewildering place. Looking in from the outside, it seems that successful and productive academic writers have knowledge that is inaccessible to novice scholars. While there is a growing literature on writing for scholarly publication, many of these publications promote writing and publishing as a straight-forward activity that anyone can achieve if they follow the rules. We argue that the specific and situated contexts in which academic writers negotiate publishing practices is more complicated and messy. In this paper, we attempt to make explicit our publishing processes to highlight the complex nature of publishing. We use autoethnographic narratives to provide discussion points and insights into the challenges of publishing peer reviewed articles. One narrative is by a doctoral student at the beginning of her publishing career, who expresses her desires, concerns and anxieties about writing for publication. The other narrative focuses on the publishing practices of a more experienced academic writer. Both are international scholars working in the Canadian context. The purpose of this paper is to explore academic publishing through the juxtaposition of these two narratives to make explicit some of the more implicit processes. Four key themes emerge from these narratives. To publish successfully, academic writers need: 1) to be discourse analysts; 2) to have a critical competence; and 3) to have writing fluency and 4) to be emotionally intelligent.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.209
GPT teacher head0.370
Teacher spread0.161 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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Same venuePreprints.orgSame topicDiscourse Analysis in Language StudiesFrench-language works237,207