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Record W3082123533 · doi:10.1126/science.369.6508.1270

Mentoring with trust

2020· article· en· W3082123533 on OpenAlexaff
René S. Shahmohamadloo

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBusinessChemistryPsychology

Abstract

fetched live from OpenAlex

I hurried downstairs to the cafeteria. At the table sat my new mentees: six eager undergraduates who had signed on to work on an 8-month aquatic toxicology project I had devised. It was a crucial piece of my Ph.D. research, and it would satisfy a key graduation requirement for the undergrads. “Starting today, I get to learn what it's like to be my Ph.D. adviser,” I thought to myself excitedly. But a few minutes into the meeting, the students broke the news: They didn't have any training in toxicology. My chest tightened. How would this ever work? > “Letting the students find their own way gave them room to grow as scientists.” My inspiration to engage undergraduates in my research had come after 2 years of working as a teaching assistant. Many of my undergraduate students had voiced the same frustrations I once had: They were expected to absorb facts and regurgitate them in exams, rinse and repeat, without any real critical thinking or opportunity to apply what they had learned. I could fill that gap, I believed, by creating a project related to my own work and enlisting undergrads as the researchers, guiding them through the process while empowering them to take the lead. My thesis adviser was supportive, knowing it would be good experience for a principal investigator (PI) hopeful like me. My department purchased the fish we would study, and a government research lab offered space for the experiments. Everything was in place—except for the students' toxicology training. I was worried. But 150 yearling rainbow trout were waiting to be picked up from the hatchery. Backing out was not an option. I reminded myself how green I had been when I was an undergrad just starting to work with a Ph.D. student. My first day in the lab, I was tasked with exposing plants to precise doses of chemicals and measuring their responses—experiments unlike any I'd done before. Despite my lack of experience, my mentor gave me a key to the plant growth chambers and walked me through how to set up and run the experiment. Then, he left me to it. He assured me that he was available to help, but he did not hover over my shoulder. I spent hours meticulously setting up the experiment—and realized 3 hours later, after checking my lab notebook, that I had dosed the plants with the wrong concentrations of chemicals. I had to throw everything out and start over. But my mentor was patient. He let me make these mistakes so I could learn from them and find my own way as a researcher. Now his example inspired me. On the students' first day in the lab, I walked them through the facilities and trained them on the protocols they would be using. Then, I let them be and stood by, ready to help. In the first few days, I noticed that some forgot to calibrate the instruments or didn't follow my instructions for dissecting the fish. My instinct was to jump in and save the day. But instead, I refrained from intervening and watched proudly as the students identified their mistakes and learned from them. Later, I put them in the driver's seat when writing up the results for publication. The students surprised me by taking the paper in a different direction than we had discussed. Again, I trusted them, and they prepared an excellent manuscript. When we reconvened in the cafeteria for a reflection meeting 6 months into the project, the students thanked me for not micromanaging them, even though it had been scary for them at first. Letting the students find their own way gave them room to grow as scientists. And in the process, I also grew as a mentor. Good mentorship means trusting your mentees' capacity and treating them as more than instruments to collect data. I hope that someday I'm able to put this approach to use as a PI running my own lab. But it can be employed at any level. Good mentorship is good mentorship, whether you're a grad student or a PI—and, when given the chance, mentees can handle the responsibility.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.230
Teacher spread0.178 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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