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Record W1488153814 · doi:10.22230/src.2011v2n2a30

Creating a Successful Online Graduate Journal: Mentoring was the Key

2011· article· en· W1488153814 on OpenAlexfundvenueaboutno aff
Kelly Edmonds

Bibliographic record

VenueScholarly and Research Communication · 2011
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
FundersUniversité du Québec à MontréalBrock UniversityConcordia UniversityUniversity of TorontoUniversity of New EnglandUniversity of AlbertaUniversity of WindsorUniversity of ReginaOffice of International Science and EngineeringUniversity of OttawaMcGill UniversityQueen's University
KeywordsPublishingWork (physics)Task (project management)Graduate studentsProcess (computing)Public relationsField (mathematics)Peer reviewLibrary scienceMedical educationWorld Wide WebSociologyPolitical scienceComputer sciencePedagogyManagementEngineeringMedicine

Abstract

fetched live from OpenAlex

This article shares the establishment and journey of a Canadian student-driven academic journal. The publication is peer-reviewed and openly accessible, and is delivered online through the Open Journal Systems (OJS) developed by the Public Knowledge Project (PKP). The article outlines the journal’s vision, history, processes, performance, challenges, and most important, mentoring practices. Created to connect and support new scholars in the field of education, the foundation of the journal was based entirely on mentoring by volunteer graduate students experienced at composing academic publications. It was thought that if students chose to pursue the onerous task of publishing, they would need support as graduate work by itself is challenging and time consuming; additionally, the publication process was considered daunting (Pasco, 2009).

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)0.026
metaresearch head score (Gemma)0.052
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: none
Teacher disagreement score0.965
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0220.010
Scholarly communication0.0350.013
Open science0.0020.012
Research integrity0.0050.011
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 head0.309
GPT teacher head0.446
Teacher spread0.136 · 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

Citations1
Published2011
Admission routes3
Has abstractyes

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