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Record W2964575418 · doi:10.1080/13562517.2019.1648411

Learning the language of craft: a publishing workshop for graduate students

2019· article· en· W2964575418 on OpenAlexaff
Matthew D. Sanscartier, Matthew S. Johnston

Bibliographic record

VenueTeaching in Higher Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsCarleton University
Fundersnot available
KeywordsPublishingMentorshipScholarshipCraftPeer reviewPublicationSociologyMedical educationPedagogyPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This article outlines a workshop orienting sociology graduate students to overcoming challenges in publishing. Although graduate students are increasingly told to publish, little guidance exists on how to best prepare them for this venture; mentorship scholarship typically assumes the professor–student relationship is the best or most appropriate site of knowledge transmission about publishing. Our workshop is a collective learning experience that can be led either by experienced graduate students or faculty, aimed at developing craft knowledge (techne) about the publishing process. Participants in our workshops reported (1) that they were a site of affect normalization, helping them to understand they were not alone in fearing anonymous peer review and receiving harsh critiques of their work from peer reviews; and (2) appreciated concrete case studies of navigating the peer review process. We encourage other departments to use or modify this workshop to normalize the publishing process for graduate students.

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)0.019
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0130.007
Scholarly communication0.0070.006
Open science0.0050.016
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0070.003

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 head0.258
GPT teacher head0.553
Teacher spread0.295 · 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.

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 routes1
Has abstractyes

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