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Record W3127702706 · doi:10.1002/bes2.1841

Keeping up with the Times: Equity Issue is Now Added to Our Self‐Reflection Worksheet for Improving Scientific Mentoring

2021· article· en· W3127702706 on OpenAlexaff
Paul Grogan, Valerie T. Eviner, Sarah E. Hobbie

Bibliographic record

VenueBulletin of the Ecological Society of America · 2021
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsWorksheetSubconsciousPsychologyEngineering ethicsReflection (computer programming)Computer scienceEngineeringMathematics educationMedicineAlternative medicine

Abstract

fetched live from OpenAlex

Mentoring is a core activity for many scientists, and yet few of us have had any formal training in how to do it well. Most of us plod along, subconsciously drawing on our own experiences of having been mentored in the past, and relying on “learning by our mistakes.” Formal reflections on the goals of mentoring, and how they can best be achieved, are rare in the literature, and yet mentoring is a fundamental process not just in the scientific training of young researchers, but also in their personal development and in building the social fabric of the scientific community. Some years ago, my colleagues Val Eviner, Sarah Hobbie, and I surveyed the mentees of Professor Terry Chapin and developed a synthesis entitled “The qualities and impacts of a great mentor — and how to improve your own mentoring.” It was originally published in the ESA Bulletin 94(2), April 2013, pages 170–176. On the basis of what we learned from that survey and our further reflections, as well as a review of the sparse literature on this topic, the above article concluded with a two-page self-assessment worksheet aimed at comprehensively identifying the fundamental features of good mentoring and providing a useful reflection guide for anyone interested in analyzing and improving their mentoring practices. The worksheet is entitled: “Mentoring self-assessment reflection exercise: Are you aware of these fundamental features of good mentoring? Which features should you focus on most to be a better mentor?” Since formulating that worksheet, sensitivities to the issues of equity, diversity, inclusion, justice, and Indigeneity have been greatly heightened among the public in many countries. Guides to improve mentoring should embrace such positive social changes. Accordingly, the revised self-assessment worksheet available here has been updated to include a new reflection question specifically focused on equity, diversity, inclusion, justice, and Indigeneity so as to raise awareness among mentors of the relevance of these important issues (Table 1; easily printable PDF version available as Appendix S1). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.011
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.989
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0060.008
Open science0.0020.006
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0180.012

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 head0.034
GPT teacher head0.326
Teacher spread0.292 · 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
DomainIncentives
GenreCommentary

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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