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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 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 teacher head, 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

Citations0
Published2021
Admission routes1
Has abstractyes

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